Abstract
RGB-D data-based Simultaneous Localization and Mapping (RGB-D SLAM) aims to concurrently estimate robot poses and reconstruct traversed environments using RGB-D sensors. Many effective and impressive RGB-D SLAM algorithms have been proposed over the past years. However, virtually all the RGB-D SLAM systems developed so far rely on the static-world assumption. This is because the SLAM performance is prone to be degraded by the moving objects in dynamic environments. In this paper, we propose a novel RGB-D data-based motion removal approach to address this problem. The approach is on-line and does not require prior-known moving-object information, such as semantics or visual appearances. We integrate the approach into the front end of an RGB-D SLAM system. It acts as a pre-processing stage to filter out data that are associated with moving objects. Experimental results demonstrate that our approach is able to improve RGB-D SLAM in various challenging scenarios.
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