Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Circular Decomposition
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jul 2023.
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Citing papers per year: 2024: 1; 2025: 3.
4 papers.
- 2025RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Explorationcited by 23
- 2024MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictionscited by 12
- 2025MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learningcited by 1
- 2025Prompting Robot Teams with Natural Languagecited by 0
- 2025Prompting Robot Teams with Natural Language29 Sep 2025
- 2025RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration9 Apr 2025
- 2025MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning3 Mar 2025
- 2024MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions23 Sep 2024