G-VOM: A GPU Accelerated Voxel Off-Road Mapping System

Timothy Overbye; Srikanth Saripalli

G-VOM: A GPU Accelerated Voxel Off-Road Mapping System

Robotics 2021-09-28 v1

Authors: Timothy Overbye , Srikanth Saripalli

Abstract

We present a local 3D voxel mapping framework for off-road path planning and navigation. Our method provides both hard and soft positive obstacle detection, negative obstacle detection, slope estimation, and roughness estimation. By using a 3D array lookup table data structure and by leveraging the GPU it can provide online performance. We then demonstrate the system working on three vehicles, a Clearpath Robotics Warthog, Moose, and a Polaris Ranger, and compare against a set of pre-recorded waypoints. This was done at 4.5 m/s in autonomous operation and 12 m/s in manual operation with a map update rate of 10 Hz. Finally, an open-source ROS implementation is provided. https://github.com/unmannedlab/G-VOM

Keywords

gpu computing simultaneous localization and mapping autonomous driving

Cite

@article{arxiv.2109.13176,
  title  = {G-VOM: A GPU Accelerated Voxel Off-Road Mapping System},
  author = {Timothy Overbye and Srikanth Saripalli},
  journal= {arXiv preprint arXiv:2109.13176},
  year   = {2021}
}

Comments

7 pages, 14 figures

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