MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality
Abstract
Operating a multi-robot fleet for simultaneous localization and mapping (SLAM) in applications such as building inspection or warehouse-aisle monitoring requires the operator to maintain spatial awareness of each robot's position and mapping state, a task that scales poorly on conventional 2D interfaces. We present MR-SLAM, a mixed reality (MR) system in which an operator wearing a Meta Quest 3 headset teleoperates three simulated TurtleBot3 robots through a passthrough view with real-world occlusion, while spatially anchored dashboard panels report mapping progress in situ. Each robot runs an independent SLAM Toolbox instance whose occupancy grid is merged in real time on a Robot Operating System 2 (ROS 2) back end. Across five 9-minute evaluation sessions, the system delivered scans at 8.83 +/- 0.16 Hz, mapped 17.9 +/- 0.8 m^2 of merged occupancy, and reached 94.7 +/- 0.5% cross-instance occupancy consistency across robot pairs. An additional session recorded 6.3 ms median transform jitter and 26.7 m^2 coverage of a 41 m^2 grid. We position MR-SLAM as a reference implementation for combining passthrough mixed reality supervision with multi-robot SLAM on consumer hardware.
Cite
@article{arxiv.2605.16432,
title = {MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality},
author = {Prakash Aryan and Cem Erdogdu and Kavinaya Kumarchokkappan and Timo Kehrer and Sebastiano Panichella},
journal= {arXiv preprint arXiv:2605.16432},
year = {2026}
}
Comments
Accepted to ICRA 2026 Workshop "MM-SpatialAI Workshop: Multi-Modal Spatial AI for Robust Navigation and Open-World Understanding"