Problem
Research questions and friction points this paper is trying to address.
multi-robot SLAM
spatial awareness
mixed reality
teleoperation
human-robot interaction
MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality
career value
211K/year
🤖 AI Summary
This work addresses the limited spatial awareness and scalability inherent in traditional 2D interfaces for multi-robot SLAM operations. The authors present the first multi-robot SLAM system leveraging a consumer-grade see-through mixed reality headset (Meta Quest 3), which overlays occupancy grid maps from each TurtleBot3 robot—generated via SLAM Toolbox—onto the physical environment through spatially anchored UI elements. These maps are fused in real time by a ROS 2 backend, enabling immersive teleoperation and intuitive monitoring. In a 9-minute evaluation, the system achieved a scan update rate of 8.83 Hz, an average mapped area of 17.9 m², and inter-robot occupancy consistency of 94.7%, substantially enhancing operational intuitiveness and system scalability. The complete implementation is publicly released.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Mixed Reality
Multi-Robot SLAM
Spatial Supervision
ROS 2
Passthrough AR
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