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Volumetric Data Fusion of External Depth and Onboard Proximity Data For Occluded Space Reduction

2021-10-21 23:00:11
Matthew Strong, Caleb Escobedo, Alessandro Roncone
arXiv_RO
 
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.11512

PDF

https://arxiv.org/pdf/2110.11512.pdf

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