Radwell, N., Johnson, S. D. , Edgar, M. P., Higham, C. F. , Murray-Smith, R. and Padgett, M. J. (2019) Deep learning optimized single-pixel LiDAR. Applied Physics Letters, 115(23), 231101. (doi: 10.1063/1.5128621)
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Abstract
Interest in autonomous transport has led to a demand for 3D imaging technologies capable of resolving fine details at long range. Light detection and ranging (LiDAR) systems have become a key technology in this area, with depth information typically gained through time-of-flight photon-counting measurements of a scanned laser spot. Single-pixel imaging methods offer an alternative approach to spot-scanning, which allows a choice of sampling basis. In this work, we present a prototype LiDAR system, which compressively samples the scene using a deep learning optimized sampling basis and reconstruction algorithms. We demonstrate that this approach improves scene reconstruction quality compared to an orthogonal sampling method, with reflectivity and depth accuracy improvements of 57% and 16%, respectively, for one frame per second acquisition rates. This method may pave the way for improved scan-free LiDAR systems for driverless cars and for fully optimized sampling to decision-making pipelines.
| Item Type: | Articles |
|---|---|
| Additional Information: | This research was also funded by H2020 European Research Council (ERC) (PhotUntangle, 804626) |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Edgar, Dr Matthew and Murray-Smith, Professor Roderick and Higham, Dr Catherine and Johnson, Dr Steven and Radwell, Dr Neal and Padgett, Professor Miles |
| Authors: | Radwell, N., Johnson, S. D., Edgar, M. P., Higham, C. F., Murray-Smith, R., and Padgett, M. J. |
| College/School: | College of Science and Engineering > School of Computing Science College of Science and Engineering > School of Physics and Astronomy |
| Journal Name: | Applied Physics Letters |
| Publisher: | AIP Publishing |
| ISSN: | 0003-6951 |
| ISSN (Online): | 1077-3118 |
| Published Online: | 02 December 2019 |
| Copyright Holders: | Copyright © 2019 The Authors |
| First Published: | First published in Applied Physics Letters 115(23):231101 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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