Pulsed lidar provides a simple direct measure of range that can be scanned to produce accurate 3D point clouds for modest computational cost. A simple implementation, however, can be subject to point-cloud artifacts particularly when sensing scenes with high dynamic range.

We discuss the origins of some of these artifacts and some possible mitigations. Of particular interest is range aliasing. By creating multiple hypotheses for the range of any given return a practical system may accurately and precisely classify aliased returns..

Date Published: 24 February 2020
PDF: 7 pages
Proc. SPIE 11299, AI and Optical Data Sciences, 112990N (24 February 2020); doi: 10.1117/12.2546976

Published in SPIE Proceedings Vol. 11299:
AI and Optical Data Sciences
Bahram Jalali; Ken-ichi Kitayama, Editor(s)