Robust Visual Odometry Using Position-Aware Flow and Geometric Bundle
Adjustment
- URL: http://arxiv.org/abs/2111.11141v1
- Date: Mon, 22 Nov 2021 12:05:27 GMT
- Title: Robust Visual Odometry Using Position-Aware Flow and Geometric Bundle
Adjustment
- Authors: Yijun Cao, Xianshi Zhang, Fuya Luo, Peng Peng, Yongjie Li
- Abstract summary: A novel optical flow network (PANet) built on a position-aware mechanism is proposed first.
Then, a novel system that jointly estimates depth, optical flow, and ego-motion without a typical network to learning ego-motion is proposed.
Experiments show that the proposed system not only outperforms other state-of-the-art methods in terms of depth, flow, and VO estimation.
- Score: 16.04240592057438
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, an essential problem of robust visual odometry (VO) is
approached by incorporating geometry-based methods into deep-learning
architecture in a self-supervised manner. Generally, pure geometry-based
algorithms are not as robust as deep learning in feature-point extraction and
matching, but perform well in ego-motion estimation because of their
well-established geometric theory. In this work, a novel optical flow network
(PANet) built on a position-aware mechanism is proposed first. Then, a novel
system that jointly estimates depth, optical flow, and ego-motion without a
typical network to learning ego-motion is proposed. The key component of the
proposed system is an improved bundle adjustment module containing multiple
sampling, initialization of ego-motion, dynamic damping factor adjustment, and
Jacobi matrix weighting. In addition, a novel relative photometric loss
function is advanced to improve the depth estimation accuracy. The experiments
show that the proposed system not only outperforms other state-of-the-art
methods in terms of depth, flow, and VO estimation among self-supervised
learning-based methods on KITTI dataset, but also significantly improves
robustness compared with geometry-based, learning-based and hybrid VO systems.
Further experiments show that our model achieves outstanding generalization
ability and performance in challenging indoor (TMU-RGBD) and outdoor (KAIST)
scenes.
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