Walmart Apollo, LLC

Zhaoliang Duan

Lingfeng Zhang

Avinash Madhusudanrao Jade

Raghava Balusu

Mingquan Yuan

Benjamin R Ellison

Han Zhang

Amit Jhunjhunwala

Srinivas Muktevi

Ashlin Ghosh

William Craig Robinson

Wei Wang

Abhinav Pachauri

Zhichun Xiao

Feiyun Zhu

Tianyi Mao

Jing Wang

Paul Lewis Lobo

William Craig Robinson, Jr

Siddhartha Chakraborty

Michael Alvin Schubert, Jr

Huanyu Zang

Vincent St Jon Cucco

Zhiwei Huang

Examples provide an archway truss for supporting devices, such as sensor devices generating sensor data for objects passing through the archway and a digital display device displaying dynamic content to users moving through the archway. The archway truss includes vertical support members attached to a horizontal top member. A central support member is reinforced to protect against cart impacts. Cameras are attached to the archway truss and positioned to capture images of objects in carts moving through the archway truss. Radio frequency identification (RFID) tag readers and other sensor devices are removably attached to the archway truss to gather item identification data for objects in the carts. Pairs of wing barriers are provided for each vertical support to block the field of view of the cameras from objects outside the lanes formed by the archway truss. An exterior covering provides padding to protect users contacting the archway truss.

Archway truss device with interactive exit lanes

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.

Methods and systems for creating reference image templates for identification of products on product storage structures of a product storage facility

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.

Systems and methods for identifying different product identifiers that correspond to the same product

In some embodiments, apparatuses and methods are provided herein useful to processing captured images of objects at a product storage facility. In some embodiments, there is provided a system for processing captured images of objects including a trained machine learning model and a control circuit. In some embodiments, the trained machine learning model is configured to process unprocessed captured images. In some embodiments, the control circuit is configured to associate each of the processed images into one of a first group, a second group, or a third group; remove at least one processed image associated with the first group from the processed images in accordance with a first processing rule; and output remaining processed images associated with the first group and processed images associated with the second group to be used to retrain the trained machine learning model.

Systems and methods for processing images captured at a product storage facility

Systems and methods of monitoring inventory of a product storage facility include an image capture device configured to move about the product storage areas of the product storage facility and capture images of the product storage areas from various angles. A computing device coupled to the image capture device obtains the images of the product storage areas captured by the image capture device and processes the obtained images of the product storage areas to detect individual products captured in the obtained images. Based on detection of the individual products captured in the images, the computing device analyzes each of the obtained images to detect one or more adjacent product storage structures (shelves, pallets, etc.) and identifies and selects a single image that fully shows a product storage structure of interest and fully shows each of the products stored on the product storage structure of interest.

Systems and methods of selecting an image from a group of images of a retail product storage area

Systems and methods of detecting and recognizing products on product storage structures of a product storage facility include an image capture device that moves about and captures images of the product storage structures at the product storage facility. A computing device processes the obtained images to detect and identify the products on the product storage structure, crops each of the identified individual products from the image to generate a plurality of cropped images and generates an image histogram template, feature vector template and location information template for each of the cropped images. The cropped images are stored in an electronic database and represent a reference model for each of the identified individual products and are stored in association with the generated image histogram template, feature vector template and location information template to facilitate recognition of products subsequently captured on the product storage structure by the image capture device.

Systems and methods of using cached images to determine product counts on product storage structures of a product storage facility

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.

Systems and methods of verifying price tag label-product pairings

Systems and methods of updating templates for use in recognizing individual products in images captured at a product storage facility include an image capture device that captures one or more images of product storage structure at a product storage facility, a computing device in communication with the image capture device, and an electronic database that stores keyword model templates and feature model templates associated with images of previously recognized individual products detected at the product storage facility. The computing device obtains the keyword and feature model templates associated with a recognized product from the electronic database, extracts the keywords from the products associated with the obtained keyword model templates, identifies products that are similar to the recognized product, and updates the keyword model template for each of the products to include must keywords and negative keywords, facilitating recognition of products in subsequent images captured by the image capture device.

Systems and methods of updating model templates associated with images of retail products at product storage facilities

Systems and methods of analyzing on-shelf price tag labels and products at a product storage facility include an image capture device that captures one or more images of one or more product storage structures at a product storage facility. A computing device communicatively coupled to the image capture device analyzes the images of the product storage structures captured by the image capture device and detects individual price tag labels and individual products located on the product storage structure. Based on the detection of the price tag labels and the products, the computing device also defines separate product storage spaces of the product storage structure, determines which price tag labels are allocated to which of the separate product storage spaces, and associates in a database the price tag labels allocated to the product storage spaces with the products stored in those product storage spaces.

Systems and methods of detecting price tags and associating the price tags with products

Systems and methods of monitoring inventory of a product storage facility include an image capture device configured to move about the product storage areas of the product storage facility and capture images of the product storage areas from various angles. A computing device coupled to the image capture device obtains the images of the product storage areas captured by the image capture device and processes the obtained images of the product storage areas to detect individual products captured in the obtained images. Based on detection of the individual products captured in the images, the computing device analyzes each of the obtained images to extract meta data from the packaging the individual products to detect one more keywords and determine the locations of the detected keywords on the packaging, and then utilize this information to predict an identity of the products associated with the packaging.

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Frisco, TX, United States of America

Zhaoliang Duan