Polylidar3D-Fast Polygon Extraction from 3D Data

Clicks: 265
ID: 115197
2020
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Abstract
Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D’s versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy.
Reference Key
castagno2020sensorspolylidar3d-fast Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jeremy Castagno;Ella Atkins;Castagno, Jeremy;Atkins, Ella;
Journal sensors
Year 2020
DOI
10.3390/s20174819
URL
https://www.mdpi.com/1424-8220/20/17/4819
Keywords
mapping lidar geometry point cloud polygon

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