期刊
JOURNAL OF ELECTRONIC IMAGING
卷 27, 期 5, 页码 -出版社
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JEI.27.5.053007
关键词
visual SLAM; point and line features; static weight; dynamic environments; visual tracking
资金
- National Key R&D Program of China [2017YFB1300400]
- National Natural Science Foundation of China-Zhejiang Joint Foundation for the Integration and Informatization [U1509202]
- Key Research and Development Program of Zhejiang [2018C01086]
Visual simultaneous localization and mapping (SLAM) based on RGB-D data has been extensively researched in the past few years and has many robotic applications. Most of the state-of-the-art approaches assume static environments. However, the static assumption is not usually true in real world environments, dynamic objects can severely degrade the SLAM performance. In order to reduce the influence of dynamic objects on camera pose estimation, this paper proposes an approach that uses static point and line features. Static weights of point and line features indicating the likelihood of features being part of static environment are estimated. According to the calculated static weights, the data associated with dynamic objects are filtered out. The remaining static point and line features are considered inputs for refined pose estimation. Experiments are conducted with challenging dynamic sequences from TUM RGB-D dataset. The results demonstrate that the proposed approach is able to effectively improve the accuracy of RGB-D SLAM in dynamic environments. (C) 2018 SPIE and IS&T
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