Navigation Performance of a LiDAR-Based CNN in a Known Environment

Julian Wang, Ali Hassani, Mathieu Joerger

Peer Reviewed

Abstract:	This paper describes the iterative design, training, validation, and statistical evaluation of a Convolutional Neural Network (CNN) for the estimation of an autonomous driving system’s (ADS) position and orientation (pose) using Light Detection and Ranging (LiDAR) data. Performance assessment is carried out in a structured, static lab environment using a LiDAR-equipped rover moving along a fixed, repeated trajectory. The LiDAR CNN performance is compared against a Landmark-Based Localization method (LBL). The LiDAR CNN and LBL have similar sample error standard deviations for position estimates. In our current implementation, the LiDAR CNN does not yet incorporate data from inertial measurement units and therefore shows higher heading angle estimation error.
Published in:	Proceedings of the 2022 International Technical Meeting of The Institute of Navigation January 25 - 27, 2022 Hyatt Regency Long Beach Long Beach, California
Pages:	742 - 752
Cite this article:	Wang, Julian, Hassani, Ali, Joerger, Mathieu, "Navigation Performance of a LiDAR-Based CNN in a Known Environment," Proceedings of the 2022 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2022, pp. 742-752. https://doi.org/10.33012/2022.18169
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