- Patent Title: Self-supervised 3D keypoint learning for monocular visual odometry
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Application No.: US17093393Application Date: 2020-11-09
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Publication No.: US12073580B2Publication Date: 2024-08-27
- Inventor: Jiexiong Tang , Rares A. Ambrus , Vitor Guizilini , Sudeep Pillai , Hanme Kim , Adrien David Gaidon
- Applicant: TOYOTA RESEARCH INSTITUTE, INC.
- Applicant Address: US CA Los Altos
- Assignee: TOYOTA RESEARCH INSTITUTE, INC.
- Current Assignee: TOYOTA RESEARCH INSTITUTE, INC.
- Current Assignee Address: US CA Los Altos
- Agency: SEYFARTH SHAW LLP
- Main IPC: G06T7/00
- IPC: G06T7/00 ; B60W60/00 ; G06N3/08 ; G06T7/246 ; G06T7/269 ; G06T7/33 ; G06T7/579 ; G06T7/73 ; G06V10/46 ; G06V10/764 ; G06V10/82 ; G06V20/56 ; G06V20/64

Abstract:
A method for learning depth-aware keypoints and associated descriptors from monocular video for monocular visual odometry is described. The method includes training a keypoint network and a depth network to learn depth-aware keypoints and the associated descriptors. The training is based on a target image and a context image from successive images of the monocular video. The method also includes lifting 2D keypoints from the target image to learn 3D keypoints based on a learned depth map from the depth network. The method further includes estimating a trajectory of an ego-vehicle based on the learned 3D keypoints.
Public/Granted literature
- US20210237774A1 SELF-SUPERVISED 3D KEYPOINT LEARNING FOR MONOCULAR VISUAL ODOMETRY Public/Granted day:2021-08-05
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