SYSTEM AND METHOD FOR SELECTING DIMENSIONING FEATURES AND DIMENSIONING OBJECTS - Patent application

Abstract

An exemplary dimensioning device includes a sensor for capturing data representative of an object, a memory configured to store a first dimensioning function and a criterion associated with the first dimensioning function and a default dimensioning function, and a processor interconnected with the sensor and the memory, wherein the processor is configured to, in response to a dimensioning request to dimension the object, obtain data representative of the object from the sensor, select a designated dimensioning function from the first dimensioning function and the default dimensioning function based on the data and the criterion, invoke the designated dimensioning function, obtain dimensions of the object, and output the dimensions of the object.

Description

[Background technology]

[0001] Objects such as cargo and parcels may require to be dimensioned, for example, prior to shipment or for storage. Different shapes and sizes of objects may be optimally dimensioned by different dimensioning functions. In addition, for billing purposes, it may be preferable to use a certified dimensioning function that is metrologically legal for trade, which can provide a certified indication of the accuracy of the resulting dimensions. In other cases, the object or environment that should be the object may not meet the criteria for using a certified dimensioning function. It may be difficult for a human operator to determine whether an object falls into one category or another and select the appropriate dimensioning function, and errors in classification may result in unacceptable dimensioning results, which may require additional time and calculations to redo the dimensioning operation. Various regulatory bodies, such as NTEP, Metrology Canada, or OIML, incorporate their own rules for certification, further complicating the evaluation of selecting the appropriate dimensioning function. Summary of the Invention

[0002]

[0002] The accompanying drawings, in which like reference numbers refer to the same or functionally similar elements throughout the separate views, together with the following detailed description are incorporated in and form a part of this specification, and serve to further illustrate embodiments of the concepts that comprise the claimed inventions and to explain various principles and advantages of those embodiments. [Brief description of the drawings]

[0003] [Figure 1] 1 is a schematic diagram of a system for selecting a dimensioning function and dimensioning an object; [Diagram 2]

[0004] FIG. 2 is a block diagram of certain internal hardware components of certain devices of FIG. 1. [Diagram 3]

[0005] 1 is a flow chart of a method for dimensioning an object. [Figure 4]

[0006] 4 is a flow chart of an exemplary method for selecting a dimensioning function in block 310 of the method of FIG. 3. [Diagram 5]

[0007] 4 is a schematic diagram of an exemplary implementation of block 315 of the method of FIG. 3. [Figure 6]

[0008] 4 is a schematic diagram of another exemplary implementation of block 315 of the method of FIG. 3. [Figure 7]

[0009] 4 is a schematic diagram of another exemplary implementation of block 315 of the method of FIG. 3. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0004]

[0010] Those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been 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.

[0005]

[0011] Components of the apparatus and methods have, where appropriate, been represented in the drawings by conventional symbols and have shown only those specific details relevant to understanding the embodiments of the invention so as not to obscure the present disclosure with details that will be readily apparent to one of ordinary skill in the art having the benefit of the descriptions herein.

[0006]

[0012] Examples disclosed herein are directed to a dimensioning device comprising a sensor for capturing data representative of an object, a memory configured to store a first dimensioning function and a criterion associated with the first dimensioning function, and a default dimensioning function, and a processor interconnected with the sensor and the memory, wherein the processor is configured to, in response to a dimensioning request to dimension the object, capture data representative of the object from the sensor, select a designated dimensioning function from the first dimensioning function and the default dimensioning function based on the data and the criterion, invoke the designated dimensioning function to capture dimensions of the object, and output the dimensions of the object.

[0007]

[0013] A further example disclosed herein is directed to a dimensioning system comprising: a server configured to store a first dimensioning function, and criteria associated with the first dimensioning function, and a default dimensioning function; and a computing device including a processor, wherein the processor is configured to, in response to a dimensioning request to dimension an object, obtain data representing the object, select a designated dimensioning function from the first dimensioning function and the default dimensioning function based on the data and the criteria, invoke the designated dimensioning function to obtain dimensions of the object, and output the dimensions of the object.

[0008]

[0014] Further examples disclosed herein are directed to a method, the method including the steps of storing a first dimensioning function and a criterion associated with the first dimensioning function, storing a default dimensioning function, and in response to a dimensioning request to dimension an object, obtaining data representing the object, selecting a designated dimensioning function from the first dimensioning functions and the default dimensioning function based on the data and the criterion, invoking the designated dimensioning function to obtain dimensions of the object, and outputting the dimensions of the object.

[0009]

[0015] 1 illustrates a system 100 for selecting a dimensioning function and dimensioning an object according to the teachings of the present disclosure. The system 100 includes a mobile computing device 104 (also referred to herein as a dimensioning device 104 or simply device 104) configured to dimension an object 108 according to a selected dimensioning function. The device 104 includes an integrated sensor or set of sensors 112, such as an image sensor (e.g., optical camera, infrared sensor, etc.), a depth sensor (e.g., LIDAR, etc.), an ambient light sensor, a proximity sensor, a temperature sensor, etc., to capture object data representative of the object 108 and environmental factors surrounding the object 108, for the purpose of enabling the device 104 to select a specified dimensioning function and dimension the object 108.

[0010]

[0016] The device 104 may be in communication with the server 116 via a communication link (shown in this example as including a wireless link). For example, the link may be provided by a wireless local area network (WLAN) deployed by one or more access points (not shown). In other examples, the server 116 is located remotely from the device 104, and thus the link may include one or more wide area networks, such as the Internet, a mobile network, and the like. The server 116 may be any suitable server environment, including, for example, multiple cooperating servers operating in a cloud-based environment. A secure communication link may be employed to ensure software sealing to reduce the chance of compromising certified functionality.

[0011]

[0017] In some examples, the system 100 may include a fixed computing device 120 configured to dimension an object 124 according to a selected dimensioning function. The fixed computing device 120 is in communication with sensors 128, such as image sensors (e.g., optical cameras, infrared sensors, etc.), depth sensors (e.g., LIDAR, etc.), ambient light sensors, proximity sensors, temperature sensors, etc., to capture object data representative of the object 124. The fixed computing device 120 may also be in communication with the server 116.

[0012]

[0018] The system 100 is generally deployed to dimension objects such as objects 108 and 124. In particular, the system 100 may hold multiple dimensioning functions, each having a separate algorithm for dimensioning the objects 108 and 124. In particular, the dimensioning functions may dimension the objects 108 and 124 with various degrees of accuracy. For example, some of the dimensioning functions may be certified by a governing body as providing output dimensions above a specified accuracy (or below a maximum allowable error). For example, the dimensioning functions may be metrologically legal for commerce in one or more regulated areas. In addition, the dimensioning functions may be associated with a different set of standards, in which the dimensioning functions may generate a result or may generate a result with a specified accuracy. Thus, the system 100 may store standards associated with each of the dimensioning functions. The system 100, and in particular the mobile computing device 104 and / or the fixed computing device 120, can select a specified dimensioning function to invoke for purposes of dimensioning the objects 108 and 124, respectively, based on the criteria and the detected data representing the objects (including the objects' environment).

[0013]

[0019] The mobile computing device 104 and the fixed computing device 120 can be employed in separate contexts to select a specified dimensioning function and to dimension a target object. For example, the mobile computing device 104 can be employed by a user to dimension an object that is heavy or irregularly shaped, located in a tight space, or under other conditions where the mobility of the device 104 is advantageous. The fixed computing device 120 can be employed in a more structured context and can have a fixed field of view based on the sensor 128. For example, the fixed computing device 120 can be employed to dimension an object that is moved along a conveyor belt 132 through the field of view of the sensor 128. This does not preclude the use of a mobile device, such as the device 104 in this case, as long as the criteria can be met.

[0014]

[0020] In operation, the mobile computing device 104 and the stationary computing device 120 function similarly to select a designated dimensioning function and dimension the objects 108 and 124, respectively, using the associated sensors 112 and 128, respectively. In particular, the selection of the designated dimensioning function may be based on criteria related to the dimensioning function and the detected object data.

[0015]

[0021] Referring now to FIG. 2, certain internal components of the mobile computing device 104, the server 116, and the stationary computing device 120 are shown.

[0016]

[0022] The device 104 includes a processor 200 interconnected with a non-transitory computer-readable storage medium, such as a memory 204. The memory 204 includes a combination of volatile memory (e.g., random access memory or RAM) and non-volatile memory (e.g., read only memory or ROM, electrically erasable programmable read only memory or EEPROM, flash memory). The processor 200 and the memory 204 may each include one or more integrated circuits.

[0017]

[0023] The memory 204 stores computer readable instructions for execution by the processor 200. In particular, the memory 204 stores an application 208 which, when executed by the processor 200, configures the processor 200 to perform various functions, discussed in more detail below, associated with selection of specified dimensioning functions and dimensioning operations by the device 104. In particular, in this example, the application 208 includes a pre-processor 212 and a logic handler 216.

[0018]

[0024] The pre-processor 212 is a module of the application 208 configured to analyze the dimensioning feature-related criteria and the detected object data to make a selection of a specified dimensioning feature. The logic handler 216 is a module of the application 208 configured to handle the business logic to make an appropriate request to the pre-processor 212 for an appropriate specified dimensioning feature. For example, the logic handler 216 may extract parameters of a dimensioning request to determine whether the specified dimensioning feature is to be qualified or perform other pre-processing or post-processing associated with a dimensioning operation. It will be understood that as used herein, the pre-processor 212 and the logic handler 216 may be said to perform various actions through execution by the processor 200 of instructions stored therein.

[0019]

[0025] In other examples, the application 208 (ie, including the pre-processor 212 and the logic handler 216) may be implemented as a suite of separate applications.

[0020]

[0026] Those skilled in the art will appreciate that the functionality performed by processor 200 may, in other embodiments, be implemented by one or more specially designed hardware and firmware components, such as a field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc. In one embodiment, processor 200 may be a special purpose processor that may be implemented via special purpose logic circuitry, such as an ASIC, FPGA, etc., to enhance processing speed of the operations discussed herein, respectively.

[0021]

[0027] The memory 204 also stores a repository 220 that stores rules and data regarding the selection of a specified dimensioning function. For example, the repository 220 may store, for each dimensioning function, a criteria or set of conditions associated with that dimensioning function. The criteria for a given dimensioning function may represent parameters for which the dimensioning function is optimized to provide the most accurate results. For a qualified dimensioning function, the criteria may represent parameters for which the resulting output dimensions may be qualified.

[0022]

[0028] The criteria may include parameters related to attributes of the object itself, the object's environment, the use of the dimensioning device 104 and / or sensors used to acquire object data, the confidence level of the dimensioning function, etc. The criteria may be defined by exclusions (i.e., conditions under which a given dimensioning function should not be used), inclusions, thresholds, and other suitable criteria.

[0023]

[0029] The object parameters in the criteria can be defined based on the shape, color and reflectance, surface characteristics of the object, etc. For example, a particular dimensional measurement function can be optimized or qualified for a particular shape of the target object, such as a cubic object. Thus, if any irregular shape or protrusion is detected, the object parameters may not be met. Similarly, particular color and reflectance attributes, or surface characteristics such as transparency, roughness, or bumpiness, can be listed as exceptions for the object parameters.

[0024]

[0030] The environmental parameters can be defined based on lighting, characteristics of the support surface, characteristics of the background, unification, etc. For example, some sensors used for a particular dimensioning function may not work well in daylight or in darkness, and therefore the environmental parameters can specify a target range of lighting levels to be detected by another sensor capable of detecting suitable lighting conditions. Some dimensioning functions may require a visible or flat support surface, sufficient space around the target object, or may have maximum background complexity, etc. Thus, the environmental parameters can specify that such environmental considerations are present or detected with respect to the use of the corresponding dimensioning function.

[0025]

[0031] The usage parameters can be defined based on the operation of the device 104 and / or the sensor capturing the object data for evaluation against the criteria. For example, some dimensioning functions may require a fixed operation of the sensor capturing the object data. The usage parameters can therefore specify exceptions when motion or changing orientation is detected. Other dimensioning functions can include motion and / or orientation correction for a certain range of motion or orientation change as part of their dimensioning function algorithms, and the usage parameters can therefore specify a threshold amount of motion or orientation change.

[0026]

[0032] The confidence parameter can be a confidence metric from the dimensioning function itself, for example, during or after the dimensioning function is performed, the dimensioning function can generate a confidence level in the calculated output dimensions.

[0027]

[0033] The memory 204 may further store at least two dimensioning functions as separate applications. In this example, the memory 204 stores a first dimensioning function 224 and a default dimensioning function 228.

[0028]

[0034] The first dimensioning function 224 can be, for example, a certified dimensioning function. In particular, certification of the dimensioning functions can require that they are software sealed and tamper-proof, and therefore corresponding applications implementing those functions can be stored in the memory 204 as separate applications. The first dimensioning function 224 can further have associated criteria stored in the repository 220.

[0029]

[0035] The memory 204 may further store additional dimensioning applications (not shown) that perform additional dimensioning functions. The memory 204, and in particular the repository 220, may further store associated criteria for the additional dimensioning functions. For the first dimensioning function 224, the associated criteria may be firm requirements for the qualification of the resulting dimensions. However, in other examples, the criteria associated with the dimensioning function may be softer goals against which the dimensioning function application is optimized.

[0030]

[0036] The default dimensioning feature 228 may not have any references associated with it. In some examples, the default dimensioning feature 228 may be instructions that are to be output for performing a manual measurement of a target object. In other examples, the default dimensioning feature 228 may be substantially the same as a certified dimensioning feature, without certification of the accuracy of the resulting dimensions. Other default dimensioning features are also contemplated.

[0031]

[0037] In this example, device 104 is shown as storing an application that performs dimensioning functions 224 and 228, although in other examples, the dimensioning functions may be stored remotely, such as as an application on server 116, and accessed or called by device 104.

[0032]

[0038] The device 104 also includes a communication interface 232 that allows the device 104 to exchange data with other computing devices, such as the server 116. The communication interface 232 is interconnected with the processor 200 and includes appropriate hardware (e.g., a transmitter, a receiver, a network interface controller, etc.) that allows the device 104 to communicate with other computing devices, such as the server 116. The particular components of the communication interface 232 are selected based on the type of network or other link over which the device 104 is to communicate. The device 104 can be configured to use the communication interface 232 to communicate with the server 116, for example, to send and receive data to and from the server 116, to invoke dimensioning functions stored on the server 116, etc.

[0033]

[0039] The device 104 may further include one or more input and / or output devices 236. The input devices may include one or more buttons, a keypad, a touch-sensitive display screen, etc. for receiving input from an operator, e.g., to initiate a dimensioning operation. The output devices may include one or more display screens, sound generators, vibrators, etc. for providing output or feedback to an operator, e.g., to output the determined dimensions of the target object.

[0034]

[0040] The server 116 includes a processor 240 interconnected with a memory 244 and a communication interface 248. The memory 244 may store additional dimensioning functions 252-1 through 252-n (generically and collectively referred to herein as dimensioning functions 252), as well as a selection application 254 and a repository 256. The dimensioning functions 252 are similar to the dimensioning functions 224 and 228 and may be associated with respective sets of criteria stored in the repository 256. The selection application 254 may configure the processor 240 to perform various functions, discussed in more detail below, related to the selection of a specified dimensioning function from the dimensioning functions 252 by the server 116.

[0035]

[0041] Computing device 120 includes a processor 260 interconnected with memory 264 and a communication interface 268. Memory 264 stores an application 272 that, when executed, causes processor 260 to perform various functions, discussed below, associated with dimensioning operations by computing device 120. Application 272 may be similar to application 208 and may include a pre-processor and logic handler (not shown). In this example, computing device 120 may not store dimensioning functions, but rather may cooperate with server 116 to select and invoke a specified dimensioning function from dimensioning functions 252 as described herein.

[0036]

[0042]

[0023] Referring now to Figure 3, the functionality performed by device 104 will be discussed in more detail. Figure 3 illustrates a method 300 for dimensioning a target object. Method 300 will be discussed in conjunction with execution of method 300 in system 100, and in particular by device 104, via execution of application 208 for dimensioning object 108. In particular, method 300 will be described with reference to components of Figures 1 and 2. In other examples, method 300 can be performed in whole or in part by other suitable devices or systems, such as computing device 120 for dimensioning object 124, or by server 116.

[0037]

[0043] The method 300 begins at block 305 where the device 104 receives a dimensioning request. The dimensioning request may be generated, for example, in response to input from a user of the device 104. In particular, the dimensioning request may be initially processed by the logic handler 216.

[0038]

[0044] In response to the dimensioning request, the device 104 selects a specified dimensioning function at block 310. In particular, rather than immediately invoking a dimensioning application, the logic handler 216 may invoke the pre-processor 212 to select the specified dimensioning function based on criteria regarding available dimensioning functions and object data regarding the target object.

[0039]

[0045] For example, referring to FIG. 4, a flow chart of an exemplary method 400 for selecting a specified dimensioning function is shown.

[0040]

[0046] In block 405, the device 104, and in particular the pre-processor 212, obtains object data representing the object 108. The object data may include characteristics of the object 108 itself, such as surface, color, shape, etc., as well as environmental factors of the object 108, such as lighting and background conditions. For example, the pre-processor 212 may control the sensor 112 to capture the object data.

[0041]

[0047] In some examples, prior to processing, the pre-processor 212 may apply error detection algorithms to verify that the object 108 exists and is captured in the object data.

[0042]

[0048] At block 410, a candidate feature is selected from the available dimensioning features. In particular, the available dimensioning features may include the first dimensioning feature 224, any further dimensioning features, and a default dimensioning feature. The pre-processor 212 may select one of the dimensioning features that has not yet been evaluated as a candidate feature, for example, by iterating through the dimensioning features in order. For example, the dimensioning features may be ordered by the accuracy of the results.

[0043]

[0049] In other examples, the pre-processor 212 may receive an indication from the logic handler 216 that the qualified dimensioning feature is preferred or required. In such examples, the pre-processor 212 may select the qualified dimensioning feature as a candidate feature, such as the first dimensioning feature 224. In still further examples, other criteria or prioritization schemes for selecting candidate features may be specified by the logic handler 216.

[0044]

[0050] In block 415 , the pre-processor 212 retrieves from the repository 220 the criteria associated with the candidate function selected in block 405 .

[0045]

[0051] In block 420, the pre-processor 212 determines whether the object data obtained in block 405 meets the criteria retrieved in block 415. In some examples, the determination in block 420 by the pre-processor 212 may implement one or more artificial intelligence (AI) algorithms. For example, for a given dimensioning function, an AI engine may be trained based on images of target objects that meet the criteria associated with the given dimensioning function. In some examples, multiple AI-based classifiers may be employed. For example, each classifier may be trained according to a separate type of criteria. That is, separate classifiers may evaluate object parameters (e.g., object shape, color and reflectance, surface characteristics, and other physical parameters), environmental parameters (e.g., lighting, support surface characteristics, background, unification, etc.), usage parameters (e.g., motion and orientation, fixed vs. moving motion, etc.), and confidence parameters (e.g., confidence metric from the dimensioning function). The classifiers may be trained and operated separately or in parallel. Artificial intelligence can be used to determine if criteria for a particular dimensioning feature are met. For example, a set of classifiers can be used to determine if an applicable dimensioning feature can be selected. Some classifiers can be simple in nature. For example, a device and algorithm may be certified as legal for commerce within a certain temperature range, while temperatures outside that range are excluded. A simple binary classification (inside or outside range) can be used to exclude certain dimensioning features. In particular, an exclusion classifier can be performed first to avoid more costly calculations.

[0046]

[0052] Other criteria can be more complex in nature, even with respect to simple classification. Computer vision algorithms can be used to identify reflectance within the captured image. In many cases, a particular dimensioning feature can exclude a particular reflectance range. Again, binary classification can be used to determine if a dimensioning feature can be selected.

[0047]

[0053] In yet further examples, other classifiers can be trained. For example, determining whether a dimensioning function can be used in an environment where a target object will be unified (separated from other objects) may require a large collection of training images. Other situations in which trained classifiers can be used include, but are not limited to, object transparency, irregular shapes, glare, lighting (including sunlight), sparse point clouds, point cloud holes, etc. Thus, the pre-processor 212 can be configured to determine the applicability of the dimensioning function rather than dimensioning the object itself, allowing an appropriate dimensioning function to be selected and invoked. The applicability determination can be based on success criteria such as the ability to meet certified dimensioning accuracy with various objects and in various environments.

[0048]

[0054] In some examples, the pre-processor 212 uses a bank of classifiers, such as decision trees, and then uses algorithms such as random forests to finalize the decision. In other cases, the pre-processor 212 can use more complex classifiers based on deep learning. These classifiers can be based on open source or proprietary custom classifiers. Examples of open source platforms are TensorFlow, H2O, Torch, Theano, etc.

[0049]

[0055] Some of the AI-based determinations may be computationally expensive, and therefore, in some examples, rather than implementing AI algorithms locally at the device 104 or device 120, the AI-based determination at block 420 may be outsourced to the server 116. For example, the determination at block 420 may be performed by the processor 240 via execution of the selection application 254. Thus, at block 420, the pre-processor 212 (or equivalent at the device 120) may transmit the object data obtained at block 405 to the server 116 for evaluation against criteria related to the candidate features and receive a determination from the server 116. In further examples, some preliminary evaluations, including both AI-based and deterministic evaluations of the object data and criteria, may be performed at the device 104 before requesting a determination from the server 116.

[0050]

[0056] If the determination at block 420 is affirmative, i.e., the object data meets the criteria for a candidate feature, the method 400 proceeds to block 425. At block 425, the pre-processor 212 selects the candidate feature as the designated dimensioning feature.

[0051]

[0057] If the determination at block 420 is negative, i.e., the object data does not meet the criteria for the candidate features, the method 400 proceeds to block 430. At block 430, the pre-processor 212 determines whether there are additional dimensional measurement features to evaluate.

[0052]

[0058] If the determination at block 430 is affirmative, ie, there are more dimensional measurement features to evaluate, the method 400 returns to block 410 for the pre-processor 212 to select another candidate feature for evaluation.

[0053]

[0059] If the determination at block 430 is negative, i.e., there are no additional dimensioning functions to evaluate, the method 400 proceeds to block 435. At block 435, the pre-processor 212 selects the default dimensioning function 228 as the specified dimensioning function.

[0054]

[0060] 3, after selecting the specified dimensioning function in block 310, the method 300 proceeds to block 315. In block 315, the device 104 invokes the specified dimensioning function.

[0055]

[0061] In some examples, block 315 may be executed by the pre-processor 212. For example, referring to FIG. 5, a schematic diagram of the execution of block 315 by the pre-processor 212 is shown. According to a first example, the specified dimension measurement function may be the first dimension measurement function 224. After selecting the specified dimension measurement function, the pre-processor 212 directly executes a call 500-1 to the first dimension measurement function 224 to cause the processor 200 to execute the first dimension measurement function 224. As a result of the execution of the first dimension measurement function 224 by the processor 200, the first dimension measurement function 224 generates a dimension 504-1. In addition, since the first dimension measurement function 224 is a qualified function, the first dimension measurement function 224 also generates a qualification indication 508 regarding the accuracy of the dimension 504-1. The pre-processor 212 may then provide the dimension 504-1 and the qualification indication 508 to the logic handler 216.

[0056]

[0062] According to another example, the specified dimensioning function can be the default dimensioning function 228. In this case, after selecting the specified dimensioning function, the pre-processor 212 performs a call 500-2 to the default dimensioning function 228 to cause the processor 200 to execute the default dimensioning function 228. As a result, the default dimensioning function 228 generates a dimension 504-2. Because the default dimensioning function 228 is not qualified, no qualification indication is generated. The pre-processor 212 can then provide the dimension 504-2 to the logic handler 216.

[0057]

[0063] In other examples, block 315 may be executed by logic handler 216. For example, referring to FIG 6, a schematic diagram of execution of block 315 by logic handler 216 is shown. After selecting the specified dimensioning function 600, pre-processor 212 may return the specified dimensioning function 600 to logic handler 216.

[0058]

[0064] This allows the logic handler 216 to make a decision as to whether to invoke the specified dimensioning function 600 or to choose to use, for example, a manual dimensioning method. For example, when the specified dimensioning function 600 is not certified but the dimensioning request indicates that certification is required, the logic handler 216 can choose to present instructions 604 on the display of the device 104 for the user to manually measure the dimensions of the object 108 instead of invoking the uncertified specified dimensioning function 600. This can allow the device 104 to conserve computing power because a dimensioning function does not need to be invoked when a suitable specified dimensioning function is not available.

[0059]

[0065] When no such problem exists, the logic handler 216 may proceed to make a call 608-1 or 608-2 to the first dimension measurement function 224 or the default dimension measurement function 228. In particular, the logic handler 216 may similarly cause the processor 200 to execute the specified dimension measurement function 600. As a result of calling the specified dimension measurement function 600, the logic handler 216 receives the dimensions 612-1 and qualification indication 616 from the first dimension measurement function 224, or the dimensions 612-2 from the default dimension measurement function 228, when applicable.

[0060]

[0066] In other examples, the specified dimensioning function may be stored outside of the device 120 (e.g., on the server 116), such as when the device 120 is performing the method 300. Referring to FIG. 7, a schematic diagram of the execution of block 315 by the device 120 is shown. After selecting the specified dimensioning function, the device 120 may make a call 700 to the server 116 via the communication interface 268. The call 700 may specify the specified dimensioning function to perform, as well as the necessary sensor data from the sensor 128 to perform the specified dimensioning function. The server 116, and in particular the processor 240, may then perform the specified dimensioning function (e.g., from the dimensioning function 252) and return the calculated dimensions 704 and, when applicable, a qualified indication 708 to the device 120.

[0061]

[0067] 3 and the execution of the method 300 by the device 104, at block 320, the logic handler 216 outputs the dimensions and, when applicable, a qualification indication of the object 108. For example, the dimensions may be displayed on a display of the device 104. In some examples, the dimensions and the qualification indication may be stored in the repository 220 in association with an identifier of the object 108.

[0062]

[0068] In the foregoing specification, specific embodiments have been described. However, those of ordinary skill in the art will understand that various modifications and changes can be made without departing from the scope of the invention as set forth in the following claims. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of the present teachings.

[0063]

[0069] Benefits, advantages, solutions to problems, and any elements that may cause any benefit, advantage, or solution to occur or become more pronounced should not be construed as critical, required, or essential features or elements of any or all claims. The invention is defined solely by the appended claims, including any amendments made during the pendency of this application, and all equivalents of those claims as issued.

[0064]

[0070] Moreover, in this document, relationship terms such as first and second, upper and lower, etc. may be used only to distinguish one entity or action from another and do not necessarily require or imply any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," "has," "having," "includes," "including," "contains," "containing," or any other variations thereof, are intended to cover a non-exclusive inclusion, whereby a process, method, article, or apparatus that comprises, has, includes, or contains a list of elements does not include only those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element preceded by "comprises a...", "has a...", "includes a...", or "contains a..." does not, without further constraints, preclude the presence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, or encompasses the element. The terms "a" and "an" are defined as one or more, unless expressly stated otherwise herein. The terms "substantially", "essentially", "approximately", "about", or any other variation thereof, are defined as "close to", as understood by one of ordinary skill in the art, and in one non-limiting embodiment, the term is defined as within 10%, in another embodiment within 5%, in another embodiment within 1%, and in another embodiment within 0.5%.The term "coupled," as used herein, is defined as connected, although not necessarily directly, and not necessarily mechanically. A device or structure that is "configured" in a particular way is at least configured in that way, but may also be configured in other ways not listed.

[0065]

[0071] It will be appreciated that some embodiments may be comprised of one or more dedicated processors (or "processing devices"), such as microprocessors, digital signal processors, customized processors, and field programmable gate arrays (FPGAs), and unique stored program instructions (including both software and firmware) that control the one or more processors to implement some, most, or all of the functions of the methods and / or apparatus described herein in conjunction with specific non-processor circuitry. Alternatively, some or all functions may be implemented by state machines that do not have stored program instructions, or in one or more application specific integrated circuits (ASICs) in which each function or some combination of some of those functions is implemented as custom logic. Of course, a combination of the two approaches may be used.

[0066]

[0072] Moreover, the embodiments can be implemented as a computer-readable storage medium having stored thereon computer-readable code for programming a computer (including, for example, a processor) to perform the methods described and claimed herein. Examples of such computer-readable storage media include, but are not limited to, hard disks, CD-ROMs, optical storage devices, magnetic storage devices, Read Only Memory (ROM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), and flash memory. Moreover, it is expected that a person of ordinary skill would be readily able to generate such software instructions and programs and ICs with minimal experimentation when guided by the concepts and principles disclosed herein, regardless of the potentially significant effort and many design choices motivated, for example, by available time, current technology, and economic considerations.

[0067]

[0073] The Abstract of the present disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. The Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, it can be understood that in the foregoing Detailed Description, various features have been grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are incorporated into the Detailed Description, with each claim standing on its own as individually claimed subject matter.

Claims

1. a sensor for capturing data representative of the object; a memory configured to store a first dimensional measurement function and a criterion associated with the first dimensional measurement function and a default dimensional measurement function; a processor interconnected with the sensor and the memory; Equipped with The processor, obtaining the data representative of the object from the sensor in response to a dimensioning request to dimension the object; selecting a designated dimensioning function from the first dimensioning function and the default dimensioning function based on the data and the criteria; Invoke the specified dimensioning function to obtain the dimensions of the object; outputting the dimensions of the object; A dimension measuring device configured to:

2. To select the specified dimensioning function, the processor: determining whether the object data satisfies the criteria associated with the first dimensioning function; and selecting the first dimensioning function as the designated dimensioning function when the data meets the criteria. Dimensional measurement device according to claim 1 , adapted to perform the following:

3. The dimension measuring device of claim 1 , wherein the processor is further adapted to output a qualification indication regarding the accuracy of the dimension of the object when the first dimension measuring function is selected as the designated dimension measuring function.

4. The dimensioning device of claim 1 , wherein the processor is adapted to select, based on the dimensioning request, to invoke the specified dimensioning function or to use a manual dimensioning method.

5. The dimension measuring device of claim 1 , wherein the criteria include one or more of an object parameter, an environmental parameter, a usage parameter, and a reliability parameter.

6. 2. The dimensioning device of claim 1, wherein the processor is adapted to implement an artificial intelligence algorithm for selecting the specified dimensioning function.

7. the memory is further configured to store one or more additional dimensional measurement features and a respective additional criterion associated with each of the additional dimensional measurement features; The dimensioning device of claim 1 , wherein the processor is adapted to select the specified dimensioning function from the first dimensioning function, the further dimensioning function, and the default dimensioning function based on the data, the criterion, and the further criterion.

8. 1. A dimension measurement system comprising: a server configured to store a default dimensioning function together with a first dimensioning function and a criterion associated with the first dimensioning function; A computing device including a processor; Equipped with The processor, In response to a dimensioning request to dimension an object, obtaining data representative of the object; selecting a designated dimensioning function from the first dimensioning function and the default dimensioning function based on the data and the criteria; Invoke the specified dimensioning function to obtain the dimensions of the object; outputting the dimensions of the object; a computing device configured to A dimension measurement system comprising:

9. To select the specified dimensioning function, the processor: determining whether the data satisfies the criteria associated with the first dimensioning function; and selecting the first dimensioning function as the designated dimensioning function when the data meets the criteria.

9. The dimensioning system of claim 8, further comprising:

10. 9. The dimensioning system of claim 8, wherein the processor is further configured to output a qualification indication regarding the accuracy of the dimension of the object when the first dimensioning function is selected as the designated dimensioning function.

11. 9. The dimensioning system of claim 8, wherein to invoke the specified dimensioning function, the processor is adapted to make a call to the server to execute the specified dimensioning function.

12. The dimensioning system of claim 8 , wherein the processor is adapted to select, based on the dimensioning request, to invoke the specified dimensioning function or to use a manual dimensioning method.

13. The dimensioning system of claim 8 , wherein the criteria include one or more of an object parameter, an environmental parameter, a usage parameter, and a reliability parameter.

14. The dimensioning system of claim 8 , wherein the processor is adapted to implement an artificial intelligence algorithm to select the specified dimensioning function.

15. the server is further configured to store one or more further dimensioning features and respective further criteria associated with each of the further dimensioning features; 9. The dimensional measurement system of claim 8, wherein the processor is adapted to select the specified dimension measurement function from the first dimension measurement function, the further dimension measurement function, and the default dimension measurement function based on the data, the criterion, and the further criterion.

16. storing a first dimensional measurement feature and a criterion associated with said first dimensional measurement feature; storing a default dimensioning function; In response to a dimensioning request to dimension an object, obtaining data representative of the object; selecting a designated dimensioning function from the first dimensioning function and the default dimensioning function based on the data and the criteria; Invoking the specified dimensioning function to obtain the dimensions of the object; outputting the dimensions of the object; A method comprising:

17. The step of selecting the specified dimension measurement function comprises: determining whether the data satisfies the criteria associated with the first dimensional measurement feature; selecting the first dimensioning function as the designated dimensioning function when the data meets the criteria; 17. The method of claim 16, comprising:

18. 20. The method of claim 19, further comprising the step of outputting a qualification indication regarding the accuracy of the dimension of the object when the first dimension measurement function is selected as the designated dimension measurement function.

19. The method of claim 16 , further comprising the step of selecting, based on the dimensioning request, to invoke the specified dimensioning function or to use a manual dimensioning method.

20. The method of claim 16 , wherein the criteria include one or more of an object parameter, an environmental parameter, a usage parameter, and a reliability parameter.

21. storing one or more further dimensioning features and respective further criteria associated with each of said further dimensioning features; selecting the designated dimensioning function from the first dimensioning function, the further dimensioning function, and the default dimensioning function based on the data, the criterion, and the further criterion; 20. The method of claim 16, further comprising:

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