Deep learning optimized single-pixel LiDAR
Radwell, Neal and Johnson, Steven D. and Edgar, Matthew P. and Higham, Catherine F. and Murray-Smith, Roderick and Padgett, Miles J. (2019) Deep learning optimized single-pixel LiDAR. Applied Physics Letters, 115 (23). 231101. ISSN 0003-6951 (https://doi.org/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.
ORCID iDs
Radwell, Neal, Johnson, Steven D. ORCID: https://orcid.org/0000-0002-6181-8303, Edgar, Matthew P., Higham, Catherine F., Murray-Smith, Roderick and Padgett, Miles J.;-
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Item type: Article ID code: 89717 Dates: DateEvent2 December 2019Published29 October 2019AcceptedSubjects: Science > Physics Department: Faculty of Science > Physics Depositing user: Pure Administrator Date deposited: 24 Jun 2024 12:34 Last modified: 12 Oct 2024 00:33 URI: https://strathprints.strath.ac.uk/id/eprint/89717