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

TL;DR

This paper introduces a probabilistic method that fuses external depth data with onboard proximity sensors to create more accurate, occlusion-reduced 3D maps of robot environments, enhancing spatial awareness.

Contribution

It extends the Octomap framework to integrate multiple sensor types, improving environmental mapping around robots with occlusion reduction.

Findings

Fused data results in more accurate 3D maps.

Reduced occlusions in the environment map.

Enhanced environmental perception for robotic navigation.

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.