Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Circular Decomposition
Abstract
This work proposes an autonomous multi-robot exploration pipeline that coordinates the behaviors of robots in an indoor environment composed of multiple rooms. Contrary to simple frontier-based exploration approaches, we aim to enable robots to methodically explore and observe an unknown set of rooms in a structured building, keeping track of which rooms are already explored and sharing this information among robots to coordinate their behaviors in a distributed manner. To this end, we propose (1) a geometric cue extraction method that processes 3D point cloud data and detects the locations of potential cues such as doors and rooms, (2) a circular decomposition for free spaces used for target assignment. Using these two components, our pipeline effectively assigns tasks among robots, and enables a methodical exploration of rooms. We evaluate the performance of our pipeline using a team of up to 3 aerial robots, and show that our method outperforms the baseline by 33.4% in simulation and 26.4% in real-world experiments.
Cite
@article{arxiv.2307.15202,
title = {Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Circular Decomposition},
author = {Seungchan Kim and Micah Corah and John Keller and Graeme Best and Sebastian Scherer},
journal= {arXiv preprint arXiv:2307.15202},
year = {2023}
}