TRAINING A MACHINE-LEARNING MODEL TO PREDICT LOCATION USING WHEEL MOTION DATA

    公开(公告)号:US20240003707A1

    公开(公告)日:2024-01-04

    申请号:US17873528

    申请日:2022-07-26

    CPC classification number: G01C21/383 G01C21/16 G01S5/0036 G07C5/04

    Abstract: A shopping cart's tracking system receives wheel motion data from a plurality of wheel sensors coupled to a plurality of wheels of the shopping cart, wherein the wheel motion data describes rotation of the plurality of wheels and orientation of the plurality of wheels. The tracking system predicts an estimated location of the shopping cart by applying a machine-learning location model to the wheel motion data. The machine-learning location model is trained with training examples that are generated by: receiving prior wheel motion data from the plurality of wheel sensors, partitioning the prior wheel motion data into a plurality of segments using a time window, receiving one or more baseline locations at one or more prior timestamps, and generating one or more training examples, each training example comprising a segment of prior wheel motion data and a baseline location with a timestamp overlapping the segment.

    SHOPPING CART SELF-TRACKING IN AN INDOOR ENVIRONMENT

    公开(公告)号:US20250058814A1

    公开(公告)日:2025-02-20

    申请号:US18937402

    申请日:2024-11-05

    Applicant: Maplebear Inc.

    Abstract: A shopping cart's tracking system determines a baseline location of the shopping cart at a first timestamp with a wireless device located on the shopping cart detecting one or more external wireless devices (e.g., RFID tags). The shopping cart's tracking system receives wheel motion data from one or more wheel sensors coupled to one or more wheels of the shopping cart, wherein the wheel motion data describes rotation and orientation of the one or more wheels. The shopping cart's tracking system calculates a translation traveled by the shopping cart from the baseline location based on the wheel motion data. The shopping cart's tracking system determines an estimated location of the shopping cart at a second timestamp based on the baseline location and the translation. The shopping cart provides functionality with the estimated location.

    SHOPPING CART SELF-TRACKING IN AN INDOOR ENVIRONMENT

    公开(公告)号:US20240001981A1

    公开(公告)日:2024-01-04

    申请号:US17873526

    申请日:2022-07-26

    CPC classification number: B62B5/0096 B62B3/1424 H04W4/029

    Abstract: A shopping cart's tracking system determines a first baseline location of the shopping cart at a first timestamp with a wireless device located on the shopping cart detecting one or more external wireless devices (e.g., RFID tags) in the indoor environment. The shopping cart's tracking system receives wheel motion data from one or more wheel sensors coupled to one or more wheels of the shopping cart, wherein the wheel motion data describes rotation of the one or more wheels. The shopping cart's tracking system calculates a translation traveled by the shopping cart from the first baseline location based on the wheel motion data. The shopping cart's tracking system determines an estimated location of the shopping cart at a second timestamp based on the first baseline location and the translation. With the estimated location, the shopping cart can update a map with the estimated location of the shopping cart.

    Shopping cart self-tracking in an indoor environment

    公开(公告)号:US12227219B2

    公开(公告)日:2025-02-18

    申请号:US17873526

    申请日:2022-07-26

    Applicant: Maplebear Inc.

    Abstract: A shopping cart's tracking system determines a first baseline location of the shopping cart at a first timestamp with a wireless device located on the shopping cart detecting one or more external wireless devices (e.g., RFID tags) in the indoor environment. The shopping cart's tracking system receives wheel motion data from one or more wheel sensors coupled to one or more wheels of the shopping cart, wherein the wheel motion data describes rotation of the one or more wheels. The shopping cart's tracking system calculates a translation traveled by the shopping cart from the first baseline location based on the wheel motion data. The shopping cart's tracking system determines an estimated location of the shopping cart at a second timestamp based on the first baseline location and the translation. With the estimated location, the shopping cart can update a map with the estimated location of the shopping cart.

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