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RGB-D SLAM in Dynamic Environments Using Static Point Weighting | IEEE Journals & Magazine | IEEE Xplore

RGB-D SLAM in Dynamic Environments Using Static Point Weighting


Abstract:

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...Show More

Abstract:

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.
Published in: IEEE Robotics and Automation Letters ( Volume: 2, Issue: 4, October 2017)
Page(s): 2263 - 2270
Date of Publication: 11 July 2017

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