In this work, we present a method for a probabilistic fusion of external depth and onboard proximity data to form a volumetric 3-D map of a robot's environment. We extend the Octomap framework to update a representation of the area around the robot, dependent on each sensor's optimal range of operation. Areas otherwise occluded from an external view are sensed with onboard sensors to construct a more comprehensive map of a robot's nearby space. Our simulated results show that a more accurate map with less occlusions can be generated by fusing external depth and onboard proximity data.
@article{arxiv.2110.11512,
title = {Volumetric Data Fusion of External Depth and Onboard Proximity Data For Occluded Space Reduction},
author = {Matthew Strong and Caleb Escobedo and Alessandro Roncone},
journal= {arXiv preprint arXiv:2110.11512},
year = {2021}
}
Comments
3 pages, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) 4th Workshop on Proximity Perception