Extending free‐space mapping to unstructured, off‐road environments

Eric D. Farmer; John E. Ball; Ali C. Gurbuz

doi:10.1117/12.2561083

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23 April 2020 Extending free‐space mapping to unstructured, off‐road environments

Eric D. Farmer, John E. Ball, Ali C. Gurbuz

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Proceedings Volume 11415, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020; 1141509 (2020) https://doi.org/10.1117/12.2561083
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Autonomous vehicles are complex robotic and artificial intelligence systems working together to achieve safe operation in unstructured environments. The objective of this work is to provide a foundation to develop more advanced algorithms for off-road autonomy. The project explores the point cloud data captured from lidar sensors, and the processing to restore some of the geometric information lost during sensor sampling. Because ground truth values are needed for quantitative comparison, the MAVS was leveraged to generate a large off-road dataset in a variety of ecosystems. The results demonstrate data capture from the sensor suite and successful reconstruction of the selected geometric information. Using this geometric information, the point cloud data is more accurately segmented using the SqueezeSeg network.

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Eric D. Farmer, John E. Ball, and Ali C. Gurbuz "Extending free‐space mapping to unstructured, off‐road environments", Proc. SPIE 11415, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020, 1141509 (23 April 2020); https://doi.org/10.1117/12.2561083

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