Rights accessRestricted access - publisher's policy
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.
CitationValencia, R.; Andrade-Cetto, J.; Porta, J.M. Path planning in belief space with Pose SLAM. A: IEEE International Conference on Robotics and Automation. "2011 IEEE International Conference on Robotics and Automation". Shangai: 2011, p. 78-83.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder. If you wish to make any use of the work not provided for in the law, please contact: email@example.com