Back to Search
Start Over
A Fast Multiplane Segmentation Algorithm for Sparse 3-D LiDAR Point Clouds by Line Segment Grouping
- Source :
- IEEE Transactions on Instrumentation and Measurement; 2023, Vol. 72 Issue: 1 p1-15, 15p
- Publication Year :
- 2023
-
Abstract
- This article describes an approach for extracting multiple planar regions in 3-D point clouds from spinning multibeam LiDARs. This technique benefits from the intrinsic structure of LiDARs and projective geometry, which allows us to extract line segments efficiently in 2-D space and then cluster those line segments to form planes. To extract planes from line primitives, we introduce a novel line segment grouping approach by alternatively searching candidate plane seeds of adjacent line segments and breadth-first searching for neighboring lines fallen on the seeded plane. Exhaustive experiments have been conducted with simulation, realistic data, and a public plane segmentation evaluation benchmark. Experimental results show that our method works well on sparse point clouds with the fastest running speed compared to state-of-the-art methods.
Details
- Language :
- English
- ISSN :
- 00189456 and 15579662
- Volume :
- 72
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Instrumentation and Measurement
- Publication Type :
- Periodical
- Accession number :
- ejs61719248
- Full Text :
- https://doi.org/10.1109/TIM.2023.3234028