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An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

1
Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS 39762, USA
2
Geotechnical and Structures Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Andrzej Stateczny
Sensors 2021, 21(9), 3211; https://doi.org/10.3390/s21093211
Received: 8 April 2021 / Revised: 26 April 2021 / Accepted: 27 April 2021 / Published: 5 May 2021
(This article belongs to the Section Sensors and Robotics)
Negative obstacles have long been a challenging aspect of autonomous navigation for ground vehicles. However, as terrestrial lidar sensors have become lighter and less costly, they have increasingly been deployed on small, low-flying UAV, affording an opportunity to use these sensors to aid in autonomous navigation. In this work, we develop an analytical model for predicting the ability of UAV or UGV mounted lidar sensors to detect negative obstacles. This analytical model improves upon past work in this area because it takes the sensor rotation rate and vehicle speed into account, as well as being valid for both large and small view angles. This analytical model is used to predict the influence of velocity on detection range for a negative obstacle and determine a limiting speed when accounting for vehicle stopping distance. Finally, the analytical model is validated with a physics-based simulator in realistic terrain. The results indicate that the analytical model is valid for altitudes above 10 m and show that there are drastic improvements in negative obstacle detection when using a UAV-mounted lidar. It is shown that negative obstacle detection ranges for various UAV-mounted lidar are 60–110 m, depending on the speed of the UAV and the type of lidar used. In contrast, detection ranges for UGV mounted lidar are found to be less than 10 m. View Full-Text
Keywords: lidar; autonomy; navigation; UAV lidar; autonomy; navigation; UAV
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MDPI and ACS Style

Goodin, C.; Carrillo, J.; Monroe, J.G.; Carruth, D.W.; Hudson, C.R. An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation. Sensors 2021, 21, 3211. https://doi.org/10.3390/s21093211

AMA Style

Goodin C, Carrillo J, Monroe JG, Carruth DW, Hudson CR. An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation. Sensors. 2021; 21(9):3211. https://doi.org/10.3390/s21093211

Chicago/Turabian Style

Goodin, Christopher, Justin Carrillo, J. Gabriel Monroe, Daniel W. Carruth, and Christopher R. Hudson. 2021. "An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation" Sensors 21, no. 9: 3211. https://doi.org/10.3390/s21093211

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