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JACIII Vol.26 No.5 pp. 731-739
doi: 10.20965/jaciii.2022.p0731
(2022)

Paper:

Coarse TRVO: A Robust Visual Odometry with Detector-Free Local Feature

Yuhang Gao and Long Zhao

School of Automation Science and Electrical Engineering, Beihang University
Beijing 100191, China

Received:
March 20, 2022
Accepted:
May 19, 2022
Published:
September 20, 2022
Keywords:
feature matching, transformer, NVIDIA TensorRT, visual odometry
Abstract
Coarse TRVO: A Robust Visual Odometry with Detector-Free Local Feature

Framework of the system

The visual SLAM system requires precise localization. To obtain consistent feature matching results, visual features acquired by neural networks are being increasingly used to replace traditional manual features in situations with weak texture, motion blur, or repeated patterns. However, to improve the level of accuracy, most deep learning enhanced SLAM systems, which have a decreased efficiency. In this paper, we propose Coarse TRVO, a visual odometry system that uses deep learning for feature matching. The deep learning network uses a CNN and transformer structures to provide dense high-quality end-to-end matches for a pair of images, even under indistinctive settings with low-texture regions or repeating patterns occupying the majority of the field of view. Meanwhile, we made the proposed model compatible with NVIDIA TensorRT runtime to boost the performance of the algorithm. After obtaining the matching point pairs, the camera pose is solved in an optimized way by minimizing the re-projection error of the feature points. Experiments based on multiple data sets and real environments show that Coarse TRVO achieves a higher robustness and relative positioning accuracy in comparison with the current mainstream visual SLAM system.

Cite this article as:
Y. Gao and L. Zhao, “Coarse TRVO: A Robust Visual Odometry with Detector-Free Local Feature,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.5, pp. 731-739, 2022.
Data files:
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Last updated on Sep. 27, 2022