RGB-D SLAM in Dynamic Environments Using Static Point Weighting

We propose a real-time depth edge based RGB-D SLAM system for dynamic environment. Our visual odometry method is based on frame-to-keyframe registration, where only depth edge points are used. To reduce the influence of dynamic objects, we propose a static weighting method for edge points in the key...

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Bibliographic Details
Published inIEEE robotics and automation letters Vol. 2; no. 4; pp. 2263 - 2270
Main Authors Li, Shile, Lee, Dongheui
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Cameras
Computer vision for other robotic applications
Dynamics
Estimation
Image edge detection
Simultaneous localization and mapping
SLAM (Simultaneous Localization and Mapping)
Three-dimensional displays
visual tracking
Visualization
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Summary:We propose a real-time depth edge based RGB-D SLAM system for dynamic environment. Our visual odometry method is based on frame-to-keyframe registration, where only depth edge points are used. To reduce the influence of dynamic objects, we propose a static weighting method for edge points in the keyframe. Static weight indicates the likelihood of one point being part of the static environment. This static weight is added into the intensity assisted iterative closest point (IAICP) method to perform the registration task. Furthermore, our method is integrated into a SLAM (Simultaneous Localization and Mapping) system, where an efficient loop closure detection strategy is used. Both our visual odometry method and SLAM system are evaluated with challenging dynamic sequences from the TUM RGB-D dataset. Compared to state-of-the-art methods for dynamic environment, our method reduces the tracking error significantly.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2017.2724759