LIDAR using a deep-learning approach

Steven Johnson; Miles J. Padgett; Catherine Higham; Roderick Murray-Smith

doi:10.1117/12.2576884

5 March 2021 LIDAR using a deep-learning approach

Steven Johnson, Miles J. Padgett, Catherine Higham, Roderick Murray-Smith

Proceedings Volume 11703, AI and Optical Data Sciences II; 117030N (2021) https://doi.org/10.1117/12.2576884
Event: SPIE OPTO, 2021, Online Only

Abstract

Using a convolutional neural network to develop an optimal sampling strategy for LIDAR remote sensing. Detecting the distance to object is important for autonomous vehicles, surveying, and other remote sensing applications. LIDAR detects distances using a pulsed laser and a time-of-flight system to measure the position of all objects in a scene, however they are limited in the maximum distance they can measure due to low signal return. A convolutional neural network has been used to develop a sampling basis to effectively sample the scene, and also the reconstruction algorithm to recreate the 3D scene.

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Steven Johnson, Miles J. Padgett, Catherine Higham, and Roderick Murray-Smith "LIDAR using a deep-learning approach", Proc. SPIE 11703, AI and Optical Data Sciences II, 117030N (5 March 2021); https://doi.org/10.1117/12.2576884

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