Research Article
Adaptive Essential Matrix Based Stereo Visual Odometry with Joint Forward-Backward Translation Estimation
@INPROCEEDINGS{10.1007/978-3-030-63083-6_10, author={Huu Hung Nguyen and Quang Thi Nguyen and Cong Manh Tran and Dong-Seong Kim}, title={Adaptive Essential Matrix Based Stereo Visual Odometry with Joint Forward-Backward Translation Estimation}, proceedings={Industrial Networks and Intelligent Systems. 6th EAI International Conference, INISCOM 2020, Hanoi, Vietnam, August 27--28, 2020, Proceedings}, proceedings_a={INISCOM}, year={2020}, month={11}, keywords={Visual odometry Essential matrix Non-iterative translation estimation Forward-backward translation estimation}, doi={10.1007/978-3-030-63083-6_10} }- Huu Hung Nguyen
Quang Thi Nguyen
Cong Manh Tran
Dong-Seong Kim
Year: 2020
Adaptive Essential Matrix Based Stereo Visual Odometry with Joint Forward-Backward Translation Estimation
INISCOM
Springer
DOI: 10.1007/978-3-030-63083-6_10
Abstract
Visual Odometry is widely used for recovering the trajectory of a vehicle in an autonomous navigation system. In this paper, we present an adaptive stereo visual odometry that separately estimates the rotation and translation. The basic framework of VISO2 is used here for feature extraction and matching due to its feature repeatability and real-time speed on standard CPU. The rotation is accurately obtained from the essential matrix of every two consecutive frames in order to avoid the affection of the stereo calibration uncertainty. With the estimated rotation, translation is rapidly calculated and refined by our proposed linear system with non-iterative refinement without the requirement of any ground truth data. The further improvement of the translation by joint backward and forward estimation is also presented in the same framework of the proposed linear system. The experimental results evaluated on the KITTI dataset demonstrate around 30% accuracy enhancement of the proposed scheme over the traditional visual odometry pipeline without much increase in the system overload.
