LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic

Tarlan Suleymanov, Matthew Gadd, Lars Kunze, and Paul Newman

Abstract: This paper presents a system for improving the robustness of LiDAR lateral localisation systems. This is made possible by including detections of road boundaries which are invisible to the sensor (due to occlusion, e.g. traffic) but can be located by our Occluded Road Boundary Inference Deep Neural Network. We show an example application in which fusion of a camera stream is used to initialise the lateral localisation. We demonstrate over four driven forays through central Oxford – totalling 40 km of driving – a gain in performance that inferring of occluded road boundaries brings – beyond that which is currently possible by na¨?ve visible road boundary detection based methods.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 334 - 341
Cite this article: Suleymanov, Tarlan, Gadd, Matthew, Kunze, Lars, Newman, Paul, "LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 334-341.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In