Rapid Autonomous Semantic Mapping
Anup Parikh, Mark W. Koch, Timothy J. Blada, Stephen P. Buerger
Abstract
A semantic understanding of the environment is needed to enable high level autonomy in robotic systems. Recent results have demonstrated rapid progress in underlying technology areas, but few results have been reported on end-to-end systems that enable effective autonomous perception in complex environments. In this paper, we describe an approach for rapidly and autonomously mapping unknown environments with integrated semantic and geometric information. We use surfel-based RGB-D SLAM techniques, with incremental object segmentation and classification methods to update the map in realtime. Information theoretic and heuristic measures are used to quickly plan sensor motion and drive down map uncertainty. Preliminary experimental results in simple and cluttered environments are reported.
BibTeX
@inproceedings{iros2020_rapidautonomouss,
title = {Rapid Autonomous Semantic Mapping},
author = {Anup Parikh and Mark W. Koch and Timothy J. Blada and Stephen P. Buerger},
booktitle = {IROS 2020},
year = {2020}
}