# Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Circular Decomposition

arXiv: https://arxiv.org/abs/2307.15202 · HTML: https://mlsanity.com/paper/2307.15202/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2023.

## Built on by (most cited first, 4 of 4)

- [RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration](https://mlsanity.com/paper/2504.06994/) (2025)
- [MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions](https://mlsanity.com/paper/2409.15590/) (2024)
- [MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning](https://mlsanity.com/paper/2503.01548/) (2025)
- [Prompting Robot Teams with Natural Language](https://mlsanity.com/paper/2509.24575/) (2025)

## 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.