A 2-point pose estimation algorithm for monocular visual odometry of ground vehicles
Yanyan Gao, Jian Chen, Kaixiang Zhang, Bingxi Jia
Abstract
This paper presents a 2-point algorithm for relative pose estimation of a monocular camera mounted on a ground vehicle. Specifically, the geometric model is developed by combining two-view geometry with planar motion constraint. Based on the proposed model, the relative camera pose information can be estimated efficiently with at least 2 feature correspondences. Besides, the proposed algorithm can recover rotation independently with translation so that pure rotation can be computed stably under planar motion. Both experiments on synthetic data and in real world are presented. In the synthetic data experiments, the proposed algorithm is compared with other well-known algorithms to illustrate its high accuracy and robustness to image noise. In addition, the great robustness of the proposed algorithm to nonplanar motion is demonstrated as well. Furthermore, experiment are carried out based on KITTI Vision Benchmark, and the result indicates that the proposed algorithm has better performance than 5-point algorithm for monocular visual odometry and has a wide range of applicability.
BibTeX
@inproceedings{iros2017_a2pointposeestim,
title = {A 2-point pose estimation algorithm for monocular visual odometry of ground vehicles},
author = {Yanyan Gao and Jian Chen and Kaixiang Zhang and Bingxi Jia},
booktitle = {IROS 2017},
year = {2017}
}