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arXiv · 2110.11512

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

Matthew Strong·Caleb Escobedo·Alessandro Roncone
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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.

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Matthew Strong, Caleb Escobedo, Alessandro Roncone. 2021-10-21. Volumetric Data Fusion of External Depth and Onboard Proximity Data For Occluded Space Reduction. https://arxiv.org/abs/2110.11512

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