Trabajo presentado al ICRA celebrado en Shanghai del 9 al 13 de mayo de 2011. ; The probabilistic belief networks that result from standard feature-based simultaneous localization and map building cannot be directly used to plan trajectories. The reason is that they produce a sparse graph of landmark estimates and their probabilistic relations, which is of little value to find collision free paths for navigation. In contrast, we argue in this paper that Pose SLAM graphs can be directly used as belief roadmaps. We present a method that devises optimal navigation strategies by searching for the path in the pose graph with lowest accumulated robot pose uncertainty, independently of the map reference frame. The method shows improved navigation results when compared to shortest paths both over synthetic data and real datasets. ; This work has been partially supported by the Spanish Ministry of Science and Innovation under projects DPI-2010-18449, DPI-2008-06022, and MIPRCV Consolider-Ingenio 2010; and by a PhD scholarship to R. Valencia from the Mexican Council of Science and Technology. ; Peer Reviewed
Path planning in belief space with pose SLAM
01.01.2011
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology
Springer Verlag | 2022
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