Deep learning optimized single-pixel LiDAR

Neal Radwell, Steven D. Johnson, Matthew P. Edgar, Catherine F. Higham, Roderick Murray-Smith, Miles J. Padgett

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

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.

Original languageEnglish
Article number231101
Number of pages5
JournalApplied Physics Letters
Volume115
Issue number23
DOIs
Publication statusPublished - 2 Dec 2019

Keywords

  • 3D imaging
  • LiDAR systems
  • sampling

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