RGB-D Visual Odometry in Dynamic Environments Using Line Features

被引：0
|
作者：
Zhang H. [1 ,2 ,3 ]
Fang Z. [2 ,3 ]
Yang G. [2 ,3 ]
机构：
[1] University of Chinese Academy of Sciences, Beijing
[2] Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo
[3] Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo
来源：
Jiqiren/Robot | 2019年 / 41卷 / 01期
关键词：
Dynamic environment; Line feature; RGB-depth; Simultaneous localization and mapping; Visual odometry;
D O I：
10.13973/j.cnki.robot.180020
中图分类号：
学科分类号：
摘要：
Most of RGB-D SLAM (simultaneous localization and mapping) methods assume that the environments are static. However, there are often dynamic objects in real world environments, which can degrade the SLAM performance. In order to solve this problem, a line feature-based RGB-D (RGB-depth) visual odometry is proposed. It calculates static weights of line features to filter out dynamic line features, and uses the rest of line features to estimate the camera pose. The proposed method not only reduces the influence of dynamic objects, but also avoids the tracking failure caused by few point features. The experiments are carried out on a public dataset. Compared with state-of-the-art methods like ORB (oriented FAST and rotated BRIEF) method, the results demonstrate that the proposed method reduces the tracking error by about 30% and improves the accuracy and robustness of visual odometry in dynamic environments. © 2019, Science Press. All right reserved.
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收藏
页码：75 / 82
页数：7
相关论文
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