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Multi-Robot Multi-Room Exploration With Geometric Cue Extraction and Circular Decomposition

Seungchan Kim, Micah Corah, John G. Keller, Graeme Best, Sebastian Scherer

Year
2023
Citations
12

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, and 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 three aerial robots, and show that our method outperforms the baseline by 33.4% in simulation and 26.4% in real-world experiments.

Keywords

RobotPipeline (software)Computer sciencePoint cloudSet (abstract data type)Artificial intelligenceDecompositionComputer visionHuman–computer interaction

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