外部深度と搭載近接センサの volumetric データ融合による遮蔽空間の削減
Volumetric Data Fusion of External Depth and Onboard Proximity Data For Occluded Space Reduction
外部深度センサとロボット搭載の近接センサのデータを確率的に融合し、Octomapを拡張して遮蔽領域の少ない3次元環境地図を生成する手法を提案した。
著者: Matthew Strong, Caleb Escobedo, Alessandro Roncone
分類: cs.RO
原文アブストラクト
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.
