Compressive super-pixel LiDAR for high-framerate 3D depth imaging

Andreas Asmann, Brian Stewart, Joao F. C. Mota, Andrew M. Wallace

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

We propose a new sampling and reconstruction framework for full frame depth imaging using synchronised, programmable laser diode and photon detector arrays. By adopting a measurement scheme that probes the environment with sparse, pseudo-random patterns, our method enables eye-safe LiDAR operation, while guaranteeing fast reconstruction of depth images with a high signal-to-noise ratio (SNR). Building up on the observation that certain quantities derived from the photon count histograms are sparse in either the ℓ1-norm or have small total variation (TV), reconstruction is performed via compressed sensing (CS) and takes approximately 30 s per frame. To speed up reconstruction, we further introduce a checkerboard tiling approach (CB-CS) that reduces the processing time to milliseconds per tile, with comparable or even less reconstruction error. Although in our experiments we reconstruct tiles sequentially at a frame rate of ~4 Hz, this process is highly parallelizable and has the potential to achieve 1 kHz frame rates.

Original languageEnglish
Title of host publication2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
PublisherIEEE
ISBN (Electronic)9781728127231
DOIs
Publication statusPublished - 27 Jan 2020
Event7th IEEE Global Conference on Signal and Information Processing 2019 - Ottawa, Canada
Duration: 11 Nov 201914 Nov 2019

Conference

Conference7th IEEE Global Conference on Signal and Information Processing 2019
Abbreviated titleGlobalSIP 2019
Country/TerritoryCanada
CityOttawa
Period11/11/1914/11/19

Keywords

  • 3D Image Reconstruction
  • Compressed Sensing
  • Parallelization
  • Solid-State Arrayed LiDAR

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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