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Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
Author to whom correspondence should be addressed.
Electronics 2018, 7(9), 154;
Received: 18 July 2018 / Revised: 9 August 2018 / Accepted: 16 August 2018 / Published: 21 August 2018
PDF [3664 KB, uploaded 21 August 2018]


Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation. View Full-Text
Keywords: perception in challenging conditions; obstacle detection and classification; dynamic path-planning algorithms perception in challenging conditions; obstacle detection and classification; dynamic path-planning algorithms

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Goodin, C.; Doude, M.; Hudson, C.R.; Carruth, D.W. Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation. Electronics 2018, 7, 154.

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