• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    A method of obtaining label positions in an image controller includes: receiving (i) a definition of a plane that includes edges of a support structure, and (ii) a plurality of initial label indicators that have locations on the plane; designating the initial label indicators from a plurality of candidate sub-sets of label indicators, each candidate sub-set representing a single physical label; for each candidate subset of initial label indicators: generating, for each of a plurality of projection depths relative to the plane, a set of projections of the initial label indicators in the candidate subset; determining a composite surface area for each set of projections; selecting, as an obtained depth for the candidate subset, one of the projection depths corresponding to the minimum composite surface area; and generating an obtained position for the candidate subset based on the obtained depth; and storing the obtained positions.
    公开号:BE1026160B1
    申请号:E20195215
    申请日:2019-04-03
    公开日:2020-07-15
    发明作者:Joseph Lam；Vlad Gorodetsky；Richard Jeffrey Rzeszutek
    申请人:Symbol Technologies Llc；
    IPC主号:
    专利说明:

    METHOD AND CALCULATOR FOR OBTAINING
    LABEL POSITIONS Background Environments that manage inventory of objects, such as products for purchase in a retail environment, can be complex and changeable. For example, a given environment can include a wide variety of objects with different attributes (size, shape, price and the like). Furthermore, the placement and quantity of the objects in the environment can change frequently. In addition, imaging conditions such as lighting may vary over time and at different locations in the environment. These factors may reduce the accuracy with which information about the objects can be collected within the environment. Certain objects, such as labels, can be especially difficult to detect accurately due to their small size and placement on or near other structures such as shelf edges. Summary In accordance with an aspect of the invention, a method of obtaining label positions in an image controller is provided, the method comprising: receiving (1) a definition of a plane comprising edges of a support structure, and (ii) a plurality of initial label indicators that have locations on the plane; assigning the initial label indicators to candidate subsets from a plurality of candidate subsets of label indicators, each candidate subset representing a single physical label; for each candidate subset of initial label indicators: generating, for each of a plurality of projection depths relative to the plane, a set of projections of the initial label indicators in the candidate subset; selecting, as an acquired depth for the candidate subset, one of the projection depths based on the sets of projections; and generating an acquired position for the candidate subset based on the acquired depth; and storing the obtained positions.
    Advantageously, each initial label indicator includes decoded data; and wherein assigning the initial label indicators to candidate subsets from a plurality of candidate subsets comprises assigning each initial label indicator to one of the candidate subsets based on the decoded data.
    Furthermore, the method may include receiving an edge indicator that defines a location of an edge of the support structure in the plane; wherein assigning the initial label indicators to candidate subsets from a plurality of candidate subsets further comprises: for initial label indicators that do not overlap with the edge indicator, assigning each initial label indicator to one of the candidate subsets based on the decoded data. Advantageously, the edge indicator is a shelf edge indicator.
    Alternatively and / or additionally, assigning the initial label indicators to candidate subsets from a plurality of candidate subsets further, for initial label indicators overlapping with the boundary indicator, assigning the initial label indicators to one of a plurality of sets based on the decoded data; determining an average position of the initial label indicators within each set; and assigning the initial label indicators within each set to the candidate
    subsets based on a distance from each of the initial label indicators to the average position.
    The method may further include: determining a composite surface area for each set of projections; and selecting, as the obtained depth, the projection depth corresponding to a minimum composite surface area.
    Also, prior to generating the sets of projections, the method may further include, for each of a plurality of coarse projection depths relative to the plane, generating a set of coarse projections of the initial label indicators in the candidate subset; selecting one of the coarse projection depths; and generating the plurality of projection depths based on the selected coarse projection depth. Advantageously, generating the multiple number of projection depths includes adding a predetermined increase to the selected coarse projection depth.
    The support structure can be, for example, a shelf or a table.
    According to another aspect of the invention, a calculator for obtaining label positions is provided, the calculator comprising: a memory; and an array controller connected to the memory, the image controller adapted to: (U) receive a definition of a plane comprising edges of a support structure, and (ii) receive a plurality of initial label indicators having locations on the plane; assign the initial label indicators to candidate subsets from a plurality of candidate subsets of label indicators, each candidate subset representing a single physical label; for each candidate subset of initial label indicators: for each of a plurality of projection depths relative to the plane, generate a set of projections of the initial label indicators in the candidate subset; as an acquired depth for the candidate subset, select one of the projection depths based on the sets of projections; and generate an acquired position for the candidate subset based on the acquisition depth; and the image controller is further arranged to store the obtained positions in the memory.
    In a further aspect of the calculator, each initial label indicator may include decoded data; and the image controller may further be arranged to assign the mitial label indicators to candidate subsets from a plurality of candidate subsets by assigning each initial label indicator to one of the candidate subsets based on the decoded data. Advantageously, the image controller may further be arranged to: receive an edge indicator that defines an edge location of the support structure in the plane; assigning the initial label indicators to candidate subsets from a plurality of candidate subsets further by: for initial label indicators that do not overlap with the edge indicator, assigning each initial label indicator to one of the candidate subsets based on the decoded data. For example, the edge indicator can be a shelf edge indicator. The image controller may further be arranged to assign the intimate label indicators to candidate subsets from a plurality of candidate subsets by, for initial label indicators overlapping the edge indicator: assign the initial label indicators to a multiple number set based on the decoded data; determining an average position of the initial label indicators within each set; and assigning the initial label indicators within each set to the candidate subset based on a distance from each of the initial label indicators to the average position.
    The image controller may further be arranged to: determine a composite surface area for each set of projections; and as the depth obtained, selecting the projection depth corresponding to a minimum composite surface area, alternatively and / or additionally, the image controller is further arranged, prior to generating the sets of projections: for each of a plurality of coarse projection depths relative to from the plane, generate a set of coarse projections of the initial label indicators in the candidate subset; select one of the coarse projection depths; and wherein the image controller is further arranged to generate the multiple number of projection depths based on the selected coarse projection depth.
    For example, the image controller may be further arranged to generate the multiple number of projection depths by adding a predetermined increase to the selected coarse projection depth. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS The accompanying figures, where like reference numerals refer to identical or functionally similar elements in the individual views, along with the figure description below, are incorporated herein and form part of the description and serve to illustrate embodiments of concepts. comprising the claimed invention to further illustrate and explain several principles and advantages of these embodiments. Herein shows: FIG. 1 a sketch of a mobile automation system; FIG. ZA is a mobile automation device in the system of FIG. 1;
    FIG. 25 is a block diagram of certain internal hardware components of the mobile automation device in the system of FIG. 1: FIG. 3 is a block diagram of certain internal components of the server of FIG. I: FIG. 4 is a flowchart of a method of obtaining label positions; FIG. 5A an example shelf arrangement; FIG. 5B recorded data and initial label indicators corresponding to the shelf of FIG. 54; FIG. 6 the initial label indicators of FIG. 5B in more detail; FIG. 7 is a flowchart of a method of assigning the label indicators to candidate subsets; FIGs SA and 8B an exemplary embodiment of the method of FIG. 7: FIGs SA through 9D an exemplary embodiment of block 420 of the method of FIG. 4: FIG, 10 is a flowchart of a method of refining projection depth for use in the method of FIG. 4, and FIG. 11A and 11B respectively enter example and results of the performance of the method of FIG. 4.
    Elements in the figures are shown for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present invention. The device and method components are represented, where appropriate, by conventional symbols in the figures, showing only those specific details that are relevant to understanding the embodiments of the present invention, otherwise obscuring the description with details that are readily apparent per se.
    Detailed Description Examples described herein are directed to a method of obtaining label positions in an image controller, the method comprising: receiving (1) a definition of a plane comprising edges of a support structure, and (ii) a multiple number of initial label indicators that have locations on the plane; designating the imaginary label indicators from a plurality of candidate sub-sets of label indicators, each candidate sub-set representing a single physical label; for each candidate subset of imitative label indicators: generating, for each of a plurality of projection depths relative to the plane, a set of projections of the initial label indicators in the candidate subset; selecting, as an acquired depth for the candidate subset, one of the projection depths based on the sets of projections; and generating an obtained position for the candidate subset based on the obtained depth; and storing the obtained positions.
    Additional examples described herein are directed to a calculator for obtaining label positions, comprising: a memory; and an image controller connected to the memory, the image controller adapted to: (1) receive a definition of a plane comprising edges of a support structure, and (11} a plurality of initial label indicators having locations on the plane; designate the initial label indicators from a plurality of candidate sub-sets of label indicators, each candidate subset representing a single physical label; for each candidate subset of initial label indicators: for each of a plurality of projection depths relative to the plane, a set of projections of the initial label indicators in the candidate subset; as an acquired depth for the candidate subset, select one of the projection depths based on the sets of projections; and generate an acquired position for the candidate subset based on the obtaining depth, and the be The controller is further arranged to store the obtained positions in the memory.
    FIG. 1 shows a mobile automation system 100 in accordance with the teachings of this description. The system 100 includes a server 101 in communication with at least one mobile automation device 103 (also referred to herein simply as the device 103) and with at least one client computing device 105 over communication links 107, which in the present example are illustrated as including wireless connections. In the present example, connections 107 are provided by a wireless local area network (WLAN) deployed in the retail environment through one or more access points (not shown). In other examples, the server 101, the client device 105, or both are located outside of retail. environment, and connections 107 therefore include wide-area networks such as the Internet, mobile networks and the like. The system 100 also includes a dock 108 for the device 103 in the present example. The dock 108 is connected to the server 101 via a connection 109 which is a wired connection in the present example. However, in other examples, the connection 109 is a wireless connection.
    The client calculator 105 is shown in FIG. 1 as a mobile calculator, such as a tablet, smartphone or the like. In other examples, the client device 105 is implemented as another type of computing device, such as a desktop computer, a laptop computer, another server, a kiosk, a monitor, and the like. The system 100 may include a plurality of client devices 105 in connection with the server 101 via connections 107.
    The system 100 is deployed in the illustrated example in a retail environment comprising a plurality of shelf modules 110-1, 110-2, 110-3 and so on (collectively referred to as shelves 110, and generally referred to as as a shelf 110 - this naming is also used for other elements discussed herein). Each shelf module 110 supports a plurality of products 112. Each shelf module 110 includes a shelf backside 116-1, 116-2, 116-3 and a support surface (e.g., support surface 117-3 as shown in FIG. 1) that extends from the plank back 116 to a plank edge 118-3, 118-2, 118-3. In other examples, the environment may include additional support structures (e.g., tables, pin boards, etc.) adjacent to or in place of the shelf modules 110.
    The shelf modules 110 are typically arranged in a multiple number of paths, each of which includes a multiple number of modules 110 aligned from end to end. In such arrangements, the shelf edges 118 face the paths, allowing customers in the retail environment as well as the device 103 to move. As will be apparent from FIG. 1, the term "shelf edge" 118 as used herein, which may also be referred to as the edge of a support surface (e.g., the support surfaces 117), refers to a surface bounded by adjacent surfaces with different angles of inclination. In the example shown in FIG. 1, the shelf edge 118-3 is angled approximately ninety degrees from each of the support surface 117-3 and the bottom (not shown) of the support surface 117-3. In other examples, the angles between the shelf edge 118-3 and the adjacent surfaces, such as the support surface 117-3, are more or less than ninety degrees. The direction 103 is deployed in the retail environment and communicates with the server 101 (e.g., through the connection 107 ) to navigate autonomously or partially autonomously along a length 119 of at least a portion of the shelves 110. The device 103 is arranged to perform such navigation relative to a frame 102 set within the retail environment. The device 103 is equipped with a plurality of navigation and data recording sensors 104, such as image sensors (e.g., one or more digital cameras) and depth sensors (e.g., one or more Light Detection and Range (LIDAR) sensors, one or more depth cameras that have structured light patterns such as infrared light, and the like}, and 1s further arranged to use the sensors 104 to record shelf data In the present example, the device 103 is arranged to record image data and depth measurements (defining a distance from a depth sensor on the device 103 to a point on the shelf 110, such as a product 112, a shelf backside 116 or the like) corresponding to the shelves 110.
    The server 101 includes a special-purpose image controller (special purpose imaging controller), such as a processor 120, which is specifically designed to control the mobile automation device 103 to record data (e.g., the aforementioned images and depth measurements). The processor 120 is further arranged to obtain the recorded data via a communication interface 124 and to store the recorded data in a memory module 132 in a memory 122 connected to the processor 120. The server 101 is further arranged for various post processing perform operations on the recorded data, which may include reporting the recorded data to frame 102 based on stored data representing positions of the device
    103 indicate the time of recording as well as calibration data defining relationships between image and depth sensors on the device 103. The process of reporting the recorded data to the frame of reference is referred to herein as back-projecting. As will be described in more detail below, the post-processing functions performed by the server 101 include recovering positional data corresponding to labels placed on the shelves 110 from the recorded data.
    The server 101 may also be configured to determine product status data (for example, to detect spaces on shelves 110}, and to send status notifications (for example, notifications that indicate that products are out of stock, low on stock, or incorrect). the client device 105 in response to the determination of product status data. The client device 105 includes one or more controllers (e.g., central processing units (CPUs) and / or field-programmable gate arrays (FPGAs) and the like) which are arranged to process notifications ( for example to show) received from the server 101.
    The processor 120 is interconnected to a non-perishable computer readable storage medium, such as the aforementioned memory 122, having stored thereon computer readable instructions for performing operation of the device 103 to record data as well as the aforementioned post-processing functionality, which is discussed in more detail below. Memory 122 includes a combination of volatile (e.g., Random Access Memory or RAM) and non-volatile memory (e.g., read only memory or ROM, Eletrically Erasable PRogrammable Read Only Memory or EEPROM, flash memory). The processor 120 and the memory 122 each comprise one or more integrated circuits. In some embodiments, the processor 120 is implemented as one or more central processing units (CPUs) and / or graphics processing units (GPUs).
    The server 101 also includes the above-mentioned communication interface 124 which is interconnected with the processor 120. The communication interface 124 includes suitable hardware (e.g. transmitters, receivers, network interface controllers and the like) that allow the server 101 to communicate with other computing devices - in particular, device 103, client device 105, and dock 108 - through connections 107 and 109. Connections 107 and 109 may be direct connections or connections that cross one or more networks, including both local and wide area networks are included. The specific components of the communication interface 124 are selected based on the type of network or other connections over which the server 101 is to communicate. In the present example, as noted previously, a wireless local area network has been implemented in the retail environment by deploying one or more wireless access points. The connections 107 therefore include wireless connections between the device 103 and the mobile device 105 and the aforementioned access points, and a wired connection (e.g. an Ethernet-based connection) between the server 101 and the access point.
    Memory 122 stores a plurality of applications, each of which includes a plurality of computer-readable instructions that can be executed by processor 120. The execution of the aforementioned instructions by the processor 120 directs the server 101 to perform various operations discussed herein, including the aforementioned obtaining of label positions. The applications stored in memory 122 include a control application 128, which can also be implemented as a set (suite) of logically different applications. Generally, the processor 120 is arranged, through execution of the control application 128 or sub-components thereof, and in conjunction with the other components of the server.
    101, implement various functionalities. The processor 120, as configured through the implementation of the control application 128, is also referred to herein as the controller 120. As will now be understood, some or all of the functionality implemented by the controller 120, described below, may also are executed by pre-provisioned hardware elements (for example one or more FPGAs and / or application specific integrated circuits (ASICs}}) instead of execution of the control application 128 by the processor 120. Referring to FIGS. ZA and 2B, the mobile automation device 103 is now shown in more detail. The device 103 comprises a chassis 201 comprising a moving mechanism 203 (for example, one or more electric motors driving the wheels, tracks or the like). The device 103 further comprises a sensor mast 205 supported on the chassis 201 and in the present example extends upward (e.g. substantially vertically} from the cha ssis 201. The mast 205 supports the sensors 104 mentioned previously. In particular, the sensors 104 include at least one image sensor 207, such as a digital camera, as well as at least one depth sensor 209, such as a 3D digital camera. The device 103 also includes additional depth sensors, such as LIDAR sensors 211. In other examples, the device 103 additional sensors, such as one or more RFID readers, temperature sensors, and the like.
    In the present example, the mast 205 supports seven digital cameras 207-1 through 207-7 and two LIDAR sensors 211-1 and 35 211-2. The mast 205 also supports a plurality of illumination assemblies 213 arranged to illuminate the fields of view of the cameras 207. That is, the lighting assembly 213-1 illuminates the field of view of the camera 207-1, and so on. The sensors 207 and 211 are oriented on the mast 205 such that the fields of view of each sensor face a shelf 110 along the length 119 along which the device 103 moves. The device 103 is arranged to track a location of the device 103 (e.g., a location of the center of the chassis 201), for example, in the frame 102 previously set in the retail facility, allowing data to be recorded is reported by the mobile automation device to frame 102.
    The mobile automation device 103 includes a special-purpose controller 220, such as a processor 220, as shown in FIG. 2B, which is interconnected with a non-perishable computer-readable storage medium, such as a memory 222. Memory 222 comprises a combination of volatile (for example, Random Access Memory or RAM) and non-volatile memory (for example, read only memory or ROM , Eletrically Erasable PRogrammable Read Only Memory or EEPROM, flash memory). The processor 220 and the memory 222 each comprise one or more integrated circuits. The memory 222 stores computer-readable instructions for execution by the processor 220. In particular, the memory 222 stores a control application 228 which, when executed by the processor 220, the processor 220 arranges to perform various functions related to the navigation of the device 103 (e.g., by controlling the moving mechanism 203) and collecting data (e.g., image and / or depth measurements) that the planks 110, The application 228 can also be implemented as a set of different applications in other examples.
    The processor 220, when so arranged by the implementation of the application 228, can also be referred to as a controller
    220. The functionality implemented by the processor 220 through the implementation of the application 288 may also be implemented by one or more specially designed hardware or firmware components, such as FPGAs, ASICs and the like in other embodiments.
    The memory 222 may also store a memory module 232 which includes, for example, a map of the environment in which the device 103 functions, for use during the execution of the application 228. The device 103 may further communicate with the server 101, for example to receive instructions to initiate data recording operations through a communication interface 224 through connection 107 shown in FIG. 1. The communication interface 224 also allows the device 103 to communicate with the server 101 through the dock 108 and the connection 109. In the present example, as described below, the server 101 (as configured through the implementation of the control application 128 by the processor 120) and / or the mobile automation device 103 (as arranged through the execution of the application 228 by the processor 220) arranged to process image and depth data recorded by the device 103 to obtain label positions (e.g. in the frame of reference 102) from the recorded data. The label positions can be stored for use in downstream processing, such as price verification, product status detection (for example, to detect a product 112 that does not match a nearby label) and the like.
    In further examples, the data processing discussed below can be performed on a computing device other than the server 101 and the mobile automation device 103, such as the screening device
    105. The aforementioned data processing will be described in more detail in relation to its execution at the server 101, through execution of the application 128, With respect to FIG. 3, before describing the operation of the application 128 to correct translucency artifacts in depth data recorded by the device 103, certain components of the application 128 are described in more detail. As will be apparent to those skilled in the art, in other examples, the components of the application 128 may be separated into different applications, or combined into other sets of components. Some or all of the components shown in FIG, 3 may also be implemented as specific hardware components, such as one or more ASICS or FPGAs.
    The control application 128 includes a candidate subset generator 300 that is arranged to obtain input data that includes initial label indicators, The initial label indicators indicate the positions of labels in the reference frame 102, but may also be affected by various error sources, Furthermore, any physical label may be shelves 110 are shown by more than one initial label indicator, for recording in successive frames of image and depth data. The candidate subset generator 300 is arranged to assign the initial label indicators to candidate subset each representing a single physical label on the shelves 110.
    The control application also includes a depth optimizer 304 which is directed to select an acquired depth for each of the candidate subsets of initial label indicators. The depth obtained strives to curb or eliminate some of the above error sources and thus represents a more accurate depth for the underlying physical label than that defined by the initial label indicators.
    The control application 128 also includes a position generator 308, which is arranged to generate acquired positions for each of the candidate subsets of initial label indicators. The acquired positions can be stored in the memory module 132 for further processing. For example, the obtained positions can be provided to a further application at the server 101 to assess planogram fulfillment (e.g., whether the labels are in positions conforming to a planogram stored in the memory module 132).
    The functionality of the control application 128 will now be described in more detail. With regard to FIG. 4, a method 400 for obtaining label positions in recorded data showing the shelves 110 is shown. The method 400 will be described along with its implementation on the system 110, and specifically with reference to the components of the server 101 shown in FIG. 3.
    At block 405, the server 101, and in particular the candidate subset generator 300 of the application 128 (as executed by the processor 120) is arranged to obtain the input data used in the execution of the method 400 to determine the obtained label positions. to generate. The input data includes a representation of a plank plane. The plank plane is a plane that includes plank edges (for example, the edges 118 shown in FIG. 1) and can be generated by a plank face generator that is performed by the processor 120 itself or by another calculator. An exemplary plank surface detector can be arranged to process depth measurements from the modules 110 or 510 (e.g., taken up by the device 103 with the depth sensor 209) to select a subset of the depth measurements indicative of plank edges (e.g. indicative of mainly vertical surfaces), and to fit a plank surface to the selected depth measurements. The plane definition obtained at block 405 may include an equation, a set of vectors or the like defining the plane, for example, according to the frame of reference 102.
    The server 101 is also arranged to obtain at block 405 a plurality of initial label indicators indicating the position of labels detected in the recorded data by the device, showing the shelves 110. Each initial label indicator includes at least one location (for example, in the frame 102). Each initial label indicator may also include data that has been decoded or otherwise obtained from the label, such as a strand decoded from a barcode printed on the corresponding label, a recognized price (e.g., through optical character recognition, OCR) from the label, and the like. The initial label indicators may have been previously generated through the execution of a label detector by the server 101 or other computing device. An example label detector may be arranged to process images from the modules 110 or 510 (e.g., captured by the device 103 with the cameras 207) to generate a property mask indicating the locations in the images that include certain predefined properties (e.g., barcodes, blocks text and the like). The label detector can then be arranged to identify locations within the property mask that correspond to predefined label templates, which indicate the likely presence of a label. Label indicators can then be generated from the above locations, for example, as bounding boxes.
    As will be seen in more detail below, at block 405, the server 101 may also be arranged to obtain data indicating the locations of plank edges (e.g., edges 118). The shelf edge locations are obtained in the present example as bounding boxes {defined, for example, by coordinates in the frame of reference 102 and falling within the above plane). The shelf edge locations may have been previously generated through the execution of a shelf structure detector by the server 101 or other computing device. An exemplary shelf edge detector can process, for example, images from the modules 110 or 510 (captured by the device 103 with the cameras 207, for example) to identify intensity transitions (transitions from light to dark and from dark to light) indicative of plank edges indicative of shelf boards. The shelf edge detector can produce bounding boxes corresponding to the areas (i.e. the likely shelf edges) bounded by such transitions,
    As noted above, example processes for the generation of the above data are set forth in corresponding applications from the applicant, and are therefore not discussed in detail herein. In the present example embodiment of the method 400, the server 101 executes block 405 by obtaining the shelf plane, imitation label indicators and optionally the shelf edge indicators, from the memory module 132, the input data previously stored in the memory module following upstream processing of data recorded by the device 103.
    Although the generation of the above input data is discussed in detail, a brief summary of exemplary mechanisms for generation of the input data from data recorded by the device 103 is provided with reference to Figures SA and 5B.
    With regard to FIG. 5A, an example shelf module 510 is shown. The module 510 includes a shelf back 516 that extends between a pair of support surfaces 517-1 and 517-2 (and also above the support surface 517-2). The support surface 517 includes edges 518-1 and 518-2. The support surface 517-2 is shown as supporting a plurality of objects 512, such as products in a retail environment. Although the support surface 517-1 is shown as not directly supporting any object, the shelf back 516 supports a plurality of pins 520, each of which can support additional products 512 above the support surface 517-1.
    The module 510 includes a plurality of labels, each corresponding to a portion of the products 512. In the illustrated example, the pins 512 carry labels 522, 524, and 526. Additionally, the shelf edge 518-2 carries labels 528, 530, 532, and 534 The data recording device 103 may be arranged {e.g. via instructions issued by the server 101) to cross the module 510 and record a plurality of images from the module 510, Two sample portions of the module 510 recorded by image frames are shown as frames 536 and 540 in FIG. 5A. As can be seen in FIG. 5A overlap the frames 536 and 540, and each portion of the module 510 is therefore shown in more than one frame received by the device 103. The device 103 is also arranged to record depth measurements during the crossing of the module 510.
    With regard to FIG. 55 shows a point cloud 550 obtained from the above depth measurements. In addition to the point cloud, a shelf face 554 is shown {generated, for example, by the above shelf face detector), as well as shelf edge indicators 558-1 and 558-2, which indicate the detected location of the shelf edges 518-1 and 518-2 in the plane 554. Also shown in FIG. 5B is a plurality of imitation label indicators generally referred to by the number 560 (a more detailed discussion of the label indicators will be provided below).
    As will be understood, the initial label indicators 560 are greater in number than the physical labels present on the module 510, as shown in FIG. SA. In the present example, the initial label indicators are detected from image data 1s recorded by the device 103. As noted above, the image frames recorded by the device 103 overlap, and therefore typically each of the labels 522 through 534 is detected in more than one image. . All label detections are stored in the memory module 132 and thus each label is represented in the input data obtained at block 405 by a plurality of initial label indicators.
    As will be appreciated, the label indicators variously include positional errors. Such errors arise from variations in image quality between the above image frames, variations in label detection accuracy, and the like. Additionally, the initial label indicators 560 corresponding to the labels 522, 524 and 526 include additional position errors resulting from their placement on the plane 554 (when the labels 522, 524 and 526 themselves are not at the same depth as the shelf edges 518). The misplacement of such label indicators may result from an assumption made by the label detector, mentioned above, that all labels are in the same plane as the shelf edges 518. Such assumptions may result from the difficulty of accurately detecting true depth of the labels from the depth measurements recorded by the device 103, due to the small size of the labels.
    In other words, instead of trying to detect a depth for the labels, the label detector can only operate on image data and the two-dimensional positions of detected label in each image frame is transformed to a three-dimensional position in the reference frame 102 by back-projecting the two-dimensional positions on the plane 552. The back projection is based, in the present example, on the stored position of the device 103 at the time of recording each image frame as well as on.
    Carrying out the rest of the method 400, the server 101 obtains unique label positions for each physical label, which may have greater accuracy than the initial label indicators shown in FIG. 55, With regard to FIG. 6, the input data obtained at block 405 is shown in more detail.
    In particular, the plane 554 is shown as well as the shelf edge indicators 558-1 and 558-2. The initial label indicators 560 shown in FIG. 55 are also shown in more detail.
    In particular, initial label indicators 622-1 and 622-2 correspond to the physical label 522 (although that fact is currently unknown to the server 101). Additionally, the label indicators 624-1 and 624-2 correspond to the label 524; the initial label indicators 626-1 and 626-2 correspond to the label 526; lahel indicators 628-1, 628-2 and 628-3 correspond to the label
    528; the label indicators 630-1 and 630-2 correspond to the label 530; the label indicators 632-1 and 632-2 correspond to the label 532; and the label indicators 634-1, 634-2 and 634-3 correspond to the label 534. Returning to FIG. 4, at block 410, the server 101, and in particular the candidate subset generator 300, is arranged to add the imitative label indicators. assign to candidate subsets. Each candidate subset corresponds to a single physical label on the shelf module 510. In other words, each candidate subset of initial label indicators includes all detections (from multiple image frames) of a given label shown in FIG. 5A.
    The assignment of initial label indicators to candidate subsets includes, in some examples, the assignment of each initial label indicator to a candidate subset based on decoded data included in the label indicator. As noted above, any label indicator may include data that has been decoded or otherwise extracted from the portion of the image in which the label was detected. The labels may include barcodes encoding product identifiers, pricing information and the like. Therefore, the assignment of each initial lahel indicator to a candidate subset may include obtaining the decoded data contained in the label indicator (e.g. stored as metadata in association with the coordinates of the label indicator) and assigning the label indicator to a candidate subset corresponding to the decoded data. Thus, each initial label indicator with the same decoded data (e.g., the same product identifier) is assigned to the same candidate subset.
    However, in some examples, more than one label appears on shelf 510 with the same barcode. In such examples, assigning initial label indicators to candidate subsets based on decoded data can only incorrectly assign indicators corresponding to different labels to the same candidate subset. The server 101 is therefore arranged in some examples to assign the initial label indicators to candidate subsets based on both the above-decoded data and relative positions of the initial label indicators.
    With regard to FIG. 7, a method 700 for assigning the label indicators to candidate subset is shown (i.e., executing block 410 of the method 400). At block 705 18, the candidate subset generator 300 is configured to determine whether each initial label indicator overlaps with a shelf edge indicator. Initial label indicators for which the determination at block 705 is negative are assigned to candidate subsets at block 710 based on decoded label data as mentioned above. Thus, the label indicators 622, 624 and 626 are shown in FIG. 6 assigned to candidate subsets only based on the decoded data they each include. In the present example, it is assumed that indicators 822 include first decoded data, indicators 624 include second decoded data, and indicators 626 include third decoded data. The above indicators are therefore assigned to three candidate subsets, with indicators 622-1 and 622-2 correspond to the label 522, and so on. Following the generation of the above candidate subsets, the candidate subset generator 300 is arranged to return the subsets for further processing (e.g., by the depth optimizer 304) at block 715. It is evident from FIG. 6 that for the other initial label indicators, the determination at block 705 is affirmative. Therefore, the candidate subset generator 300 proceeds to block 720 for those initial label indicators. At block 720, the generator 300 is arranged to place the initial label indicators in sets based on decoded label data as described above. For the initial label indicators 632 and 634, it is assumed in the present example that they all contain the same decoded data and therefore all become the same set placed. FIG. SA illustrates the set resulting from the execution of block 720. As will be understood, the set shown in FIG. SA not with a single physical label, but with two labels and should therefore be further subdivided.
    At block 725, the generator 300 determines for each set the average location in the frame 102 (i.e., on the plane 554) of the label indicators in the set. FIG. SA illustrates the average location 800 of the set of label indicators that include indicators 632 and 634. The generator 300 is also arranged to determine the distance from the average 800 to each initial label indicator (e.g., to the center of each label indicator). The above distances are shown as dotted lines in FIG. 8A At block 730 1, the generator 300 is arranged to place the initial label indicators of each set into candidate subsets based on the above distances. For example, the generator 300 may be arranged to group the initial label indicators in the set based on a threshold distance. FIG. 8B illustrates the application of a threshold distance (shown as a circle) to the distances determined in FIG. SA, to divide the set into two candidate subsets 832 and 834 (corresponding to the initial label indicators 632 and 634, respectively). As is evident from FIG. 8B, the distances for each candidate subset 832 and 834 are separated from each other by distances less than the threshold, but an individual member of a subset is separated from a member of the other subset by a distance greater than the threshold. As will now be appreciated, the process set forth in relation to blocks 720 through 730 may not be suitable for label indicators that do not overlap with a shelf edge indicator, as such label indicators may correspond to labels attached to pins 520 or other structures whose actual depth differs substantially from the depth of the plane 554. As a result, the positions of such label indicators on the plane 554 may be too inaccurate to use average locations and distances to assign the label indicators to subsets.
    Following the execution of block 730, the generator 300 is arranged to repeat the above process for each set generated at block 720 (via block 735). When each set is subdivided into candidate subsets, the candidate subsets are returned for further processing at block 715. It is possible that a candidate subset generated through the execution of blocks 725 and 730 is identical to the set initially generated at block 720. In the case of for example, the initial label indicators 628 (which are assumed to each contain the same decoded data different from the decoded data of other indicators shown in FIG. 6), the above distances may all fall within the threshold, and the initial label indicators 628 are therefore assigned to a single candidate subset.
    Returning to FIG. 4, at block 415, the depth optimizer is arranged to select a candidate subset for further processing. After a candidate subset is selected, the depth optimizer is arranged to determine an acquired depth for the candidate subset. As noted above, the depth of all initial label indicators is the depth of the shelf face 554. However, the actual labels represented by the initial label indicators are not necessarily at the same depth as the plane 554. As noted previously, the depth of the pin-attached labels 522, 524 and
    526 vary significantly with respect to the depth of the shelf edges 518. Furthermore, the plane 554 itself cannot represent an exact fit with the shelf edges 518, and so the depth assigned to the mitial label indicators may not be accurate even for initial label indicators that have labels placed on represent the shelf edges 518.
    The depth optimizer is arranged to determine an obtained depth for each candidate subset starting at block 420, by generating a plurality of projection sets. Each projection set includes a back-projection of each initial label indicator in the candidate subset to a depth different from the depth of the shelf plane 554, Each projection set 18 generated for a different set of predetermined depths. For example, the series of predetermined depths can start at the depth of the shelf face 554 and increase in increments of | mm until a depth equal to the (previously known or detected from recorded depth measurements) depth of the plank back 516. A variety of other depth increments may also be considered depending on the size of the plank 510, the desired accuracy of the depth obtained and such.
    With regard to FIG. 9A-9D, the initial label indicators 622-1 and 622-2 (both members of a single candidate subset) are shown in FIG. 9A, FIGS, 9B, 9C and 9D show successive projection sets 900-1 and 900-2, 904-1 and 904-2, and 908-1 and 908-2, Each projection set 900, 904 and 908 is the result of the back - projection of the label indicators 622 to a greater depth than the previous projection set (i.e., the set 908 is at a greater depth than the set 904), Example projection depths relative to the depth of the plane 554 are reported in each of FIGs. 9A-9D.
    As shown in Figures SA-9D, as projection depth increases, projections of indicators 622-1 and 622-2 begin to overlap. That is, the projections begin to occupy the same three-dimensional space in the frame 102, which is to be expected since both indicators 622 represent the same physical label. Thus, the projection set with the greatest degree of overlap is most likely to correspond to a corrected depth for the indicators 622.
    The optimizer 304 is therefore arranged to also determine, at block 420, the composite surface area of each projection set (i.e., the total surface area of the projection set}. At block 425, the optimizer 304 is configured to select the projection depth of the projection set with the smallest composite surface area Returning to FIGs, 9A-9D, it is evident that the final projection set (FIG. 8D) has the smallest composite surface area of those shown, therefore optimizer 304 is arranged to select the depth at which projection set 908 was generated. (i.e., 30 cm beyond the depth of the plane 554 in this example).
    As will now be apparent, a significant number of projection sets may be required to cover a sufficient range of depths to identify an acquired depth that likely reflects the true depth of a given label. For example, when using projection depths with 1 mm increments as mentioned above, three hundred projection sets are required to arrive at the projection set 908 shown in FIG. 9D. Therefore, the optimizer 304 is arranged to use both coarse and fine depth increases in some examples to generate the projection sets. With reference to FIG. 10, a projection depth refinement method 1000 is shown, for execution prior to block 420. In particular, at block 1005, the optimizer 304 is arranged to generate, in some examples, a plurality of coarse projection aggregates and determine the composite surface area of each projection set. The generation of projection sets is as described above in relation to block 420, with the exception that the depth increases separating each projection set are greater than those used at block 420. For example, if an increase of 1 mm is used at block 420, an increase of 2cm are used at block 1005, At block 1010, the optimizer 308 is arranged to select the depth of the coarse projection set having the smallest composite surface area, and at block 1015, the optimizer 304 is configured to determine the range of depths to be used at block 420 according to the coarse depth selected at block 1010, For example, if the selected coarse depth was 24 cm from the plane 554, at block 1015, the optimizer 304 may be arranged to generate a series of projection depths with increments of imm centered on a depth of 24 cm. In some examples, the method 100 can be used for only certain candidate subsets. For example, the optimizer may be configured to perform the method 100 prior to block 420 for label indicators that do not overlap with the shelf edge indicators 568 (i.e., label indicators likely to correspond to pin-attached labels).
    Returning to FIG. 4, at block 40, the position generator 308 is arranged to generate an obtained position for the candidate subset selected at block 415, based on the obtained depth. For example, the position generator 308 may be configured to generate an acquired position that includes the acquired depth (i.e., along the Z axis of the reference frame 102) as well as the coordinates (e.g., along the X and Y axes of the reference frame 102) of the center of the composite surface area of the projection set selected at block 425. Referring to FIG. 11A, portions of the plane 554 and shelf edge indicator 558-1 are shown together with the label indicators 622,
    624, and 626. FIG. 11, meanwhile, shows back projections 1122, 1124 and 1126 corresponding to indicators 622, 624 and 626, respectively. The position of each back projection 1122, 1124 and 1126 includes the depth at which each projection was generated as well as the center of the back projection shown. As is evident from a comparison of FIG. 11B with FIGS. 5A and 5B, the obtained positions of the back projections 1122, 1124 and 1126 correspond more accurately to the positions of the labels 522, 524 and 526 than the initial label indicators 560.
    Returning to FIG. 4, at block 435, the server 101 determines whether candidate subsets remain to be processed. If the determination at block 435 is affirmative, the execution of blocks 415 through 430 is repeated for each remaining candidate subset. If the determination at block 435 is negative, the execution of method 400 proceeds to block 440, where the obtained positions are generated at block 430, for example, in the memory module 132.
    For example, the obtained positions can be stored in final label indicators which not only include the obtained positions, but also the decoded data mentioned above and other data extracted from the images recorded by the device 103 (e.g. price information and the like). The final label indicators may include a plurality of decoded data strands, prices, and the like (e.g., one for each initial label indicator from which the final label indicator was generated).
    Variations of the above systems and methods are contemplated. For example, the depth optimizer 304 is configured in some examples to dynamically determine minimum and maximum depths for which projection sets are generated at block 420. For example, the depth optimizer may be arranged to obtain an expected actual size for a label based on the decoded data. The depth optimizer may further be arranged to determine an estimated depth error based on the size and size of the initial label indicator obtained (for example, the two sizes may indicate that the depth of the imitative label indicator is likely about 20 cm above or below the actual depth of the label). The depths used at block 420 (or via the method 1000) can be determined to cross the depth thus determined.
    Specific embodiments have been described in the foregoing description. It is noted, however, that various modifications and changes can be made without departing from the scope of the invention as set out in the claims below. Accordingly, the description and figures are to be considered in an illustrative rather than a limiting sense, and all such adaptations are intended to be included within the scope of the present teachings.
    The benefits, solutions to problems, and any element that could lead to any benefit, or solution that occurs or becomes more pronounced, should not be interpreted as a critical, necessary, or essential measure or element of any or all of the claims. The invention is defined only by the appended claims including modifications made while the pending of this application and all equivalents of those granted claims. For the purpose of clarity and a brief description, features herein are described as part of the same or separate embodiments. It is to be noted, however, that the scope of the invention may include embodiments that have combinations of all or some of the features described herein. It may be assumed that the embodiments shown include like or equivalent components, except where described as otherwise.
    In addition, in this document, relative terms such as first and second, top and bottom, and the like can only be used to distinguish one entity or action from another entity or action without necessarily necessitating any of these factual relationships or orders between such entities or actions is or is implied.
    The terms "includes", "comprising", "has", "having", "contains", "containing" or any other variation thereof, are intended to cover a non-exclusive inclusion such that a process, method, article , or device that includes a list of elements includes not only those elements, but may also include other elements not expressly stated or inherent in such a process, method, article, or device.
    An element preceded by “includes… a”, “has… a”, “contains… a” does not, without limitation, exclude the existence of additional identical elements in the process, the method, the article, or the device that element includes, has, contains.
    The term "one" is defined as one or more, unless expressly stated otherwise herein. The terms "substantially", "essential", "about", or any other version thereof, are defined as being close by, as understood by one skilled in the art, and in one non-limiting embodiment, the term is defined to be within 10%, in another embodiment to be within 5%, in another embodiment to be within 1%, and in another embodiment to be within 0.5%. The term "linked" as used herein is defined as connected, although not necessarily direct and not necessarily mechanical.
    Their device or structure that is "configured" in some way is configured in at least that way, but it can also be configured in ways not indicated.
    It is noted that some embodiments may include one or more generic or specialized processors (or "processing devices"), such as microprocessors, digital signal processors,
    customized processors, and field programmable gate arrays (FFGAs) and uniquely stored program instructions (including both software and hardware) that control the one or more processors, to implement some, most, or all functions in combination with certain non-processor circuits of the method and / or device described herein. Alternatively, some or all of the functions could be implemented by a state machine that has no stored program instructions, or in one or more application-specific integrated circuits (ASICs), in which each function or some combinations of certain functions are implemented as custom logic (on custom made). Obviously, a combination of the two approaches could be used.
    In addition, an embodiment can be implemented as a computer readable storage medium having stored thereon a computer readable code for programming a computer (e.g., including a processor) to perform a method as described and claimed herein. Examples of such computer readable storage media include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (read-only memory), a PROM (programmable read-only memory ), an EPROM (erasable programmable read-only memory), an EEPROM (electrically erasable programmable read-only memory) and a flash memory. It is further noted that despite potentially significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles described herein, one will be easily able to generate such software instructions and programs and ÌCs with minimal experimentation.
    The summary of the description is provided to enable the reader to quickly understand the nature of the technical description. It is submitted on the assumption that it will not be used to interpret the claims or to limit the scope of protection thereof. in the foregoing extended description, several measures have been grouped together in various embodiments for the purpose of streamlining the description.
    This method of description is not to be interpreted as reflecting an intention that the embodiments claimed require more measures than are expressly enumerated in each claim.
    Instead, as the following claims express, the subject matter of the invention lies in less than all of the features of a single described embodiment.
    So the following conclusions have been incorporated into the detailed description, with each conclusion standing on its own as separate claimed matter.
    The mere fact that certain measures are mentioned in mutually different claims does not indicate that a combination of these measures cannot be used for an advantage.
    A large number of variants will be clear to the skilled person.
    All variants are considered to be included within the scope of the invention as defined in the following claims.
    权利要求:
    Claims (18)
    [1]
    A method of obtaining label positions in an image controller, the method comprising: receiving () a definition of a plane that includes edges of a support structure, and (1) a plurality of initial label indicators having locations on the plane; assigning the initial label indicators to candidate subsets from a plurality of candidate subsets of label indicators, each candidate subset representing a single physical label; for each candidate subset of initial label indicators: generating, for each of a plurality of projection depths relative to the plane, a set of projections of the initial label indicators in the candidate subset; selecting, as an acquired depth for the candidate subset, one of the projection depths based on the sets of projections; and generating an obtained position for the candidate subset based on the obtained depth; and storing the obtained positions.
    [2]
    The method of claim 1, wherein each imitation label indicator includes decoded data; and wherein assigning the initial label indicators to candidate subsets from a plurality of candidate subsets comprises assigning each initial label indicator to one of the candidate subsets based on the decoded data,
    [3]
    The method of claim 2, further comprising receiving an edge indicator that defines a location of an edge of the support structure in the plane; wherein assigning the initial label indicators to candidate subsets from a plurality of candidate subsets further comprises: for initial label indicators that do not overlap with the edge indicator, assigning each initial label indicator to one of the candidate subsets based on the decoded data.
    [4]
    The method of claim 3, wherein the edge indicator is a shelf edge indicator.
    [5]
    The method of claim a or 4, wherein assigning the initial label indicators to candidate subsets from a plurality of candidate subsets further comprises, for initial label indicators overlapping with the edge indicator: assigning the initial label indicators to one of a multiple number of collections based on the decoded data; determining an average position of the initial label indicators within each set; and assigning the initial label indicators within each set to the candidate subset based on a distance from each of the initial label indicators to the average position.
    [6]
    The method of any of the preceding claims, further comprising 235 determining a composite surface area for each set of projections; and selecting, as the obtained depth, the projection depth corresponding to a minimum composite surface area.
    [7]
    The method of any of the preceding claims, further comprising, prior to generating the sets of projections: generating, for each of a plurality of coarse projection depths relative to the plane, a set of coarse projections of the initial label indicators in the candidate subset; selecting one of the coarse projection depths; and generating the plurality of projection depths based on the selected coarse projection depth.
    [8]
    The method of claim 7, wherein generating the plurality of projection depths includes adding a predetermined increment to the selected coarse projection depth.
    [9]
    The method of any of the preceding claims, wherein the support structure is one of a shelf and a table.
    [10]
    A known device for obtaining label positions, comprising: a memory; and an image controller connected to the memory, the image controller being arranged to: (1) receive a definition of a plane comprising edges of a support structure, and (11) receive a plurality of initial label indicators having locations on the plane; assign the initial label indicators to candidate subsets from a plurality of candidate subsets of label indicators, each candidate subset representing a single physical label; for each candidate subset of initial label indicators: for each of a multiple number of projection depths relative to the plane, generate a set of projections of the initial label indicators in the candidate subset;
    if an acquired depth for the candidate subset, select one of the projection depths based on the sets of projections; and generate an acquired position for the candidate subset based on the acquisition depth; and the image controller is further arranged to store the obtained positions in the memory,
    [11]
    The calculator of claim 10, wherein each initial label indicator includes decoded data; and wherein the image controller is further arranged to assign the initial label indicators to candidate subsets from a plurality of candidate subsets by assigning each initial label indicator to one of the candidate subsets based on the decoded data.
    [12]
    The computing apparatus of claim 11, wherein the image controller is further adapted to: receive an edge indicator defining an edge location of the support structure in the plane; assigning the initial label indicators to candidate subsets from a plurality of candidate subsets further by: for initial label indicators that do not overlap with the edge indicator, assigning each initial label indicator to one of the candidate subsets based on the decoded data.
    [13]
    The calculator of claim 12, wherein the edge indicator is a shelf edge indicator.
    [14]
    The computing apparatus of claim 12 or 13, wherein the image controller is further adapted to assign the initial label indicators to candidate subsets from a plurality of number candidate subsets by, for initial label indicators overlapping the edge indicator: assign the initial label indicators to one of a plurality of sets based on the decoded data; determining an average position of the imitative label indicators within each set; and assigning the initial label indicators within each set to the candidate subset based on a distance from each of the initial label indicators to the average position.
    [15]
    The computing device of any one of claims 10-14, wherein the image controller is further adapted to: determine a composite surface area for each set of projections; and as the obtained depth, select the projection depth corresponding to a minimum composite surface area,
    [16]
    Calculator according to any of claims 10-15, wherein the image controller is further arranged, prior to generating the projection sets: for each of a plurality of coarse projection depths relative to the plane, to generate a set of coarse projections of the imitative label indicators in the candidate subset; select one of the coarse projection depths; and wherein the image controller is further arranged to generate the multiple number of projection depths based on the selected coarse projection depth.
    [17]
    The known device of claim 16, wherein the image controller is further adapted to generate the plurality of projection depths by adding a predetermined increment to the selected coarse projection depth.
    [18]
    Calculator according to any of claims 10-17, wherein the support structure is one of a shelf and a table.
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    法律状态:
    2020-08-26| FG| Patent granted|Effective date: 20200715 |
    优先权:
    申请号 | 申请日 | 专利标题
    US15/945,926|US10832436B2|2018-04-05|2018-04-05|Method, system and apparatus for recovering label positions|
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