Rut depth detection for automated trafficability assessment

Ian Q. Mattson; Zach D. Jeffries; Casey D. Majhor; Jeremy P. Bos

doi:10.1117/12.2664429

13 June 2023 Rut depth detection for automated trafficability assessment

Ian Q. Mattson, Zach D. Jeffries, Casey D. Majhor, Jeremy P. Bos

Proceedings Volume 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023; 125400N (2023) https://doi.org/10.1117/12.2664429
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The passing of a wheeled or tracked vehicle over soft or deformable soil creates ruts. The depth of these ruts is proportional to the weight of the vehicle and the soil trafficability; the ability of the soil to support traffic from vehicles. Assessing soil trafficability is often a manual and labor-intensive process. We evaluate the ability of lidar and depth cameras to detect changes in rut depth with the goal of minimizing manual or automated evaluation via soil strength testing. Our sensor-based approach mimics the process used by human operators when measuring rut depth. We compare this approach with machine-centered approaches with the goal of improving correlation between soil strength measurements and rut depth. In general, we find that all sensors are able to measure rut depth within the uncertainty bounds of soil and rut depth models for light vehicles.

Conference Presentation

Citation Download Citation

Ian Q. Mattson, Zach D. Jeffries, Casey D. Majhor, and Jeremy P. Bos "Rut depth detection for automated trafficability assessment", Proc. SPIE 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, 125400N (13 June 2023); https://doi.org/10.1117/12.2664429

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