HIERARCHICAL SEGMENT-BASED MAP OPTIMIZATION FOR LOCALIZATION AND MAPPING SYSTEM

    公开(公告)号:US20240212204A1

    公开(公告)日:2024-06-27

    申请号:US18568676

    申请日:2021-08-31

    CPC classification number: G06T7/73 G06T7/11 G06T2207/20021 G06T2207/20072

    Abstract: The disclosure provides techniques for map optimization for a localization and mapping system. The map optimization method includes segmenting, based on a preset segmentation condition, a trajectory tracked by the localization and mapping system to obtain a plurality of segments of the trajectory, each segment being partitioned into a head part, an interior part and a tail part; performing a global optimization process based on frames in the head and tail parts of each segment to obtain optimized mapping results for the frames in the head and tail parts of the segment; estimating optimized mapping results for frames in the interior part of each segment based on the optimized mapping results for the frames in the head and tail parts of the segment; and updating a map built by the localization and mapping system according to the optimized mapping results for the frames in each segment.

    RE-LOCALIZATION OF ROBOT
    2.
    发明公开

    公开(公告)号:US20240029300A1

    公开(公告)日:2024-01-25

    申请号:US18254181

    申请日:2020-12-25

    Abstract: A method for re-localization of the robot may include retrieving, for each of keyframes in a keyframe database of the robot, image features and a pose of the keyframe, the image features of the keyframe comprising a global descriptor and local descriptors of the keyframe (210); extracting image features of a current frame captured by the robot, the image features of the current frame comprising a global descriptor and local descriptors of the current frame (220); determining one or more rough matching frames from the keyframes based on comparison between the global descriptor of each keyframe and the global descriptor of the current frame (230); determining a final matching frame from the one or more rough matching frames based on comparison between the local descriptors of each rough matching frame and the local descriptors of the current frame (240); and calculating a pose of the current frame based on a pose of the current frame based on a pose of the final matching frame (250).

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