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• W3090894413 abstract "We introduce a novel lidar-monocular visual odometry approach using point and line features. Compared to previous point-only based lidar-visual odometry, our approach leverages more environment structure information by introducing both point and line features into pose estimation. We provide a robust method for point and line depth extraction, and formulate the extracted depth as prior factors for point-line bundle adjustment. This method greatly reduces the features' 3D ambiguity and thus improves the pose estimation accuracy. Besides, we also provide a purely visual motion tracking method and a novel scale correction scheme, leading to an efficient lidar-monocular visual odometry system with high accuracy. The evaluations on the public KITTI odometry benchmark show that our technique achieves more accurate pose estimation than the state-of-the-art approaches, and is sometimes even better than those leveraging semantic information." @default.
• W3090894413 created "2020-10-08" @default.
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• W3090894413 date "2020-05-01" @default.
• W3090894413 modified "2023-10-18" @default.
• W3090894413 title "Lidar-Monocular Visual Odometry using Point and Line Features" @default.
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• W3090894413 doi "https://doi.org/10.1109/icra40945.2020.9196613" @default.
• W3090894413 hasPublicationYear "2020" @default.
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