3D Mapping in Tunnel Environments: Maximizing Mission Range

Steven Waslander, Arun Das, Mark Haley


This paper presents an analysis of the power and computational requirements for 3D tunnel mapping using an autonomous ground vehicle and a single Microsoft Kinect sensor.  A comparison of existing mapping algorithms, including RGB-D SLAM and KinectFusion, is performed on a representative hallway to demonstrate the accuracy with which tunnels can be mapped. The power requirements for both the vehicle locomotion and onboard computation are assessed, and representative tunnel mapping ranges are computed.  Finally, a system is proposed that minimizes power consumption without sacrificing map quality that could operate continuously for 26 hours and collect over x km of map data, which could be processed online for real-time mapping and refined offline for more accurate results. Extension to UAV/UGV teams is also considered.


Autonomous ground vehicles; 3D mapping; reconstruction; SLAM

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