Odometry-Vision-Based Ground Vehicle Motion Estimation With SE(2)-Constrained SE(3) Poses
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AbstractThis paper focuses on the motion estimation problem of ground vehicles using odometry and monocular visual sensors. While the keyframe-based batch optimization methods become the mainstream approach in mobile vehicle localization and mapping, the keyframe poses are usually represented by SE(3) in vision-based methods or SE(2) in methods based on range scanners. For a ground vehicle, this paper proposes a new SE(2)-constrained SE(3) parameterization of its poses, which can be easily achieved in the batch optimization framework using specially formulated edges. Utilizing such a parameterization of poses, a complete odometry-vision-based motion estimation system is developed. The system is designed in a commonly used structure of graph optimization, providing high modularity and flexibility for further implementation or adaptation. Its superior performance in terms of accuracy on a ground vehicle platform is validated by real-world experiments in industrial indoor environments.
All Author(s) ListFan Zheng, Hengbo Tang, Yun-Hui Liu
Journal nameIEEE Transactions on Cybernetics
Year2018
PublisherIEEE
ISSN2168-2267
eISSN2168-2275
LanguagesEnglish-United Kingdom
KeywordsVisualization, Optimization, Motion estimation, Estimation, Land vehicles, Manifolds, Sensors, Mobile robots, sensor fusion, state estimation

Last updated on 2020-30-10 at 01:31