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Title: Practical Persistence Reasoning in Visual SLAM
Many existing SLAM approaches rely on the assumption of static environments for accurate performance. However, several robot applications require them to traverse repeatedly in semi-static or dynamic environments. There has been some recent research interest in designing persistence filters to reason about persistence in such scenarios. Our goal in this work is to incorporate such persistence reasoning in visual SLAM. To this end, we incorporate persistence filters [1] into ORB-SLAM, a well-known visual SLAM algorithm. We observe that the simple integration of their proposal results in inefficient persistence reasoning. Through a series of modifications, we improve this persistence filtering. Using two locally collected datasets, we demonstrate the utility of such persistence filtering as well as our customizations in ORB-SLAM. Overall, incorporating persistence filtering could result in a significant reduction in map size (about 30% in the best case) and a corresponding reduction in run-time while retaining similar accuracy to methods that use much larger maps.  more » « less
Award ID(s):
1846320
PAR ID:
10167136
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE International Conference on Robotics and Automation
ISSN:
1049-3492
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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