Abstract: Examples provide a system for exit computer vision basket matching receipts to shopping carts automatically in real-time as a customer is exiting a retail facility. Computer vision analysis of one or more images of a shopping cart produces a set of recognized items from the images. The set of recognized items are compared to the items identified in each candidate receipt in a plurality of candidate receipts. A confidence score is generated for each candidate receipt based on the number of common items found in both the set of recognized items and each receipt. The score is weighted based on factors such as uniqueness of the items and number of instances of each item. The receipt having the highest weighted confidence score is paired with the shopping cart. The paired receipt and image of the shopping cart are output to a user for verification via a user interface.

Abstract: Systems and methods of verifying pairings of price tag labels and products at a product storage facility include an image capture device that moves about and captures images of product storage structures at the product storage facility, and a database that stores inventory data associated with the products stocked at the product storage facility. A computing device processes the images to extract one or more characters from the price tag labels and products detected in the images, correlates the extracted characters to the inventory data to identify a product code and a product name that matches the characters extracted from the price tag labels and the products. If the characters extracted from the price tag and from the on-shelf product to which the price tag label is allocated match, the database is updated to reflect that the price tag label is properly allocated to the appropriate on-shelf product.

Abstract: Examples provide a system and method for identifying unpaid items in real-time using computer vision object detection and recognition models. Images of a cart are selected based on proximity of the cart to one or more anchor points. The object detection and recognition models analyze the image data and identify a set of items in the selected cart. An e-receipt including a set of paid items is selected from a plurality of active electronic receipts based on matching the set of identified items to the set of paid items. Any unmatched items are added to a set of predicted unpaid items. When a receipt corresponding to the selected e-receipt is scanned, a notification identifying the set of predicted unpaid items is provided to a user device for display, enabling a user to identify any unpaid items in a basket of items quickly and accurately in real-time at store exit.

Abstract: Examples provide a system for filtering the contents of adjacent item storage compartments from item recognition results obtained using computer vision (CV) for more accurate mapping of item locations within a retail environment. A filter manager selects a vertical member and tracks it throughout a series of images generated by an image capture device. The selected vertical member defines at least a portion of a target compartment in each image. The vertical member can be a display case door or a vertical steel bar defining the side of an item storage compartment. A set of adjacent items located on each side of the target compartment is filtered from the item recognition results. The target compartment location is determined based on a location tag of the target compartment. The items remaining after filtering are mapped to the target compartment location while reducing CV item location false positives.

Abstract: Examples enable pallet tag tracking and cluster voting for more accurate pallet tag management using images of a selected pallet. A tag manager tracks a pallet through multiple images of the pallet to ensure the same pallet appears in every image. If the pallet tag is absent from all the images, a tag missing confidence score is generated that indicates the degree of confidence that the tag is missing from the pallet and not merely out of view. The score is used to prioritize handling of pallet tag missing exceptions. If the pallet tag is present in the images, optical character recognition (OCR) results for each tag image are aggregated into a tag cluster with a confidence score calculated for each result. A pallet tag identification (ID) number is predicted based on the result having the highest confidence score to ensure the pallet tag ID is complete and accurate.

Abstract: Examples provide for pallet classification and pallet tag text recognition. The system includes a pallet text manager that classifies a type of pallet tag based on detected lines of text in the pallet tag using a classification model. Qualified lines of text are selected from the detected lines of text based on the classification type and corresponding format of the text. Each qualified line of text is associated with a pallet attribute, such as a pallet identifier (ID), an item ID, or a date of creation of the pallet tag. Attribute values from the set of qualified lines of text are paired with location data for the current location of the pallet. The attribute values and the paired location data are saved in a pallet attribute table. The pallet attributes are used to identify the location of pallets in a retail facility with improved accuracy and efficiency.

Abstract: Aspects of the disclosure provide for managing a fleet of autonomous vehicles of a transportation service. For instance, a plurality of inputs including a current demand for services, predictions about future demand for services, and current status of the fleet may be identified. The current status may include information identifying one of a plurality of predefined states for each autonomous vehicle of the fleet. A schedule may be determined based on the plurality of inputs. The schedule may define a number of autonomous vehicles that should be in each of the plurality of expected future states. The schedule may be used to determine an assignment for each of the autonomous vehicles to one of the plurality of predefined states.

Abstract: A support structure for manufacturing a pressure vessel, the support structure configured to be carried on a supporting substrate, the support structure comprising a first segment support structure nestable within, and moveable relative to, a second segment support structure, wherein the first segment support structure is configured to be raised and lowered relative to the second segment support structure to thereby raise and lower the first segment relative to the substrate, and the first segment support structure is configured to move between a nested position within the second segment support structure to a position spaced apart from the second segment support structure across a surface of the substrate.

Abstract: Aspects of the disclosure provide for depot behaviors for autonomous vehicles. For instance, a signal to control an autonomous vehicle to a depot area may be received from a server computing device. A prioritized list of staging areas within the depot area may be identified. Each staging area of the prioritized list of staging areas enables the vehicle to observe stopping locations at which a need of the vehicle may be addressed. The vehicle may be controlled to a first staging area of the prioritized list. Once the vehicle has reached the first staging area, whether a stopping location that meets one or more needs of the vehicle is available may be determined. When is available, the vehicle may be controlled to the available stopping location. When not available, the vehicle may be controlled to a second staging area of the prioritized list.

Abstract: A communication, safety, and location system for use as part of a goods transporting system comprises a plurality of tile units 2, the tile units 2 configured to tesselate to form a substantially continuous grid with a substantially flat upper surface and to enable movement of cargo carrying units positioned on the surface in use; a number of the tile units at locations spaced within the grid equipped with UWB transceivers.

Abstract: Some embodiments provide retail product ordering systems comprising: a user computing device comprising an application executed by a device control circuit to: receive an audible utterance; controls a product identifier application interface to: apply a tokenizer model and obtain a set of individual search words; apply a series of featurizer models to the search words to generate features; and apply a classifier and extractor model based on the features and generate multiple requested product entities each comprising a respective sub-set of the position labeled product terms; wherein the device control circuit is further configured to access a purchase history database, confirm an accuracy of each of requested product entities relative to a purchase history, generate a listing of determined product identifiers corresponding to the confirmed set of the multiple requested product entities, and control a display system of the user computing device to render the listing of determined product identifiers.

Abstract: A support structure for manufacturing a pressure vessel, the support structure configured to be carried on a supporting substrate, the support structure comprising a first segment support structure nestable within, and moveable relative to, a second segment support structure, wherein the first segment support structure is configured to be raised and lowered relative to the second segment support structure to thereby raise and lower the first segment relative to the substrate, and the first segment support structure is configured to move between a nested position within the second segment support structure to a position spaced apart from the second segment support structure across a surface of the substrate.

Abstract: Some embodiments provide retail product ordering systems comprising: a user computing device comprising an application executed by a device control circuit to: receive an audible utterance; controls a product identifier application interface to: apply a tokenizer model and obtain a set of individual search words; apply a series of featurizer models to the search words to generate features; and apply a classifier and extractor model based on the features and generate multiple requested product entities each comprising a respective sub-set of the position labeled product terms; wherein the device control circuit is further configured to access a purchase history database, confirm an accuracy of each of requested product entities relative to a purchase history, generate a listing of determined product identifiers corresponding to the confirmed set of the multiple requested product entities, and control a display system of the user computing device to render the listing of determined product identifiers.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may group a plurality of product identifiers into one or more clusters based on at least one of visual similarity of corresponding images, textual similarity of corresponding associated descriptions, and associated relationships between product identifiers of the plurality of product identifiers; determine clusters having common elements that are at least within a similarity threshold of each other; merge the clusters with the common elements; and generate a mapping dataset used to retrain the trained machine learning model to identify a plurality of objects. The mapping dataset may include a plurality of associations of associated product identifiers to a single object.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model stored in a memory; and a control circuit. The control circuit may obtain an image at the product storage facility; cluster objects depicted in the image that have same product identifiers into a corresponding group; determine coordinates of each bounding box of each clustered object in the corresponding group; determine a bounding box representative depth value of pixels inside the bounding box of each clustered object; determine an overall representative depth value of the corresponding group based on bounding box representative depth values of clustered objects; and exclude the clustered objects from identified objects in the image upon a determination that the overall representative depth value is greater than a threshold.

Abstract: Systems and methods of detecting support members of product storage structures that store products at a product storage facility include an image capture device that captures images of a product storage structure including vertical and horizontal support members. A computing device including a control circuit is configured to: obtain the images of the product storage structure captured by the image capture device, stitch the obtained images together to generate a stitched image that depicts the product storage structure, generate a color distribution map of the stitched image of the product storage structure to detect individual ones of the horizontal and vertical support members of the product storage structure.

Abstract: Systems and methods of creating reference template images for detecting and recognizing products at a product storage facility include an image capture device having a field of view that includes a product storage structure of the product storage facility, and a computing device including a control circuit and being communicatively coupled to the image capture device. The computing device obtains images of the product storage structure captured by the image capture device, analyzes the obtained images to detect individual ones of the products located on the product storage structure. Then, the computing device identifies the individual ones of the products detected in the images and crops each of the individual ones of the identified products from the images to generate cropped images. The computing device then creates a cluster of the cropped images, and selects one of the cropped images as a reference template image of an identified individual product.

Abstract: One example method involves generating a calibration control signal that causes an actuator to rotate a first platform at least one complete rotation about an axis. The method also involves receiving encoder output signals. The encoder output signals are indicative of angular positions of the first platform about the axis. The method also involves receiving sensor output signals from an orientation sensor mounted on the first platform. The sensor output signals are indicative of a rate of change to an orientation of the orientation sensor. The method also involves determining calibration data based on given sensor output signals received from the orientation sensor during the at least one complete rotation. The calibration data is for mapping the encoder output signals to calibrated measurements of the angular positions of the first platform about the axis.

Abstract: A method of updating a clock associated with an autonomous vehicle includes determining, at a processor, a sensor-based position of the autonomous vehicle. The sensor-based position is determined based on data from one or more sensors and based on a digital map accessible to the processor. The method also includes determining, at the processor, a global positioning system (GPS) computed position of the autonomous vehicle and a GPS time. The GPS computed position and the GPS time are determined based on a plurality of GPS signals received from GPS satellites. The method further includes comparing the sensor-based position to the GPS computed position to determine whether the sensor-based position is within a threshold distance from the GPS computed position. The method also includes updating the clock associated with the autonomous vehicle based on the GPS time in response to a determination that the sensor-based position is within the threshold distance from the GPS computed position.

Abstract: Systems and methods of verifying pairings of price tag labels and products at a product storage facility include an image capture device that moves about and captures images of product storage structures at the product storage facility, and a database that stores inventory data associated with the products stocked at the product storage facility. A computing device processes the images to extract one or more characters from the price tag labels and products detected in the images, correlates the extracted characters to the inventory data to identify a product code and a product name that matches the characters extracted from the price tag labels and the products. If the characters extracted from the price tag and from the on-shelf product to which the price tag label is allocated match, the database is updated to reflect that the price tag label is properly allocated to the appropriate on-shelf product.