Next Article in Journal
Indirect Recognition of Predefined Human Activities
Previous Article in Journal
Indication of Electromagnetic Field Exposure via RBF-SVM Using Time-Series Features of Zebrafish Locomotion
Article

Polylidar3D-Fast Polygon Extraction from 3D Data

Robotics Institute, University of Michigan, Ann Arbor, MI 48105, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(17), 4819; https://doi.org/10.3390/s20174819
Received: 23 July 2020 / Revised: 11 August 2020 / Accepted: 20 August 2020 / Published: 26 August 2020
(This article belongs to the Section Sensors and Robotics)
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. View Full-Text
Keywords: point cloud; LiDAR; geometry; polygon; mapping point cloud; LiDAR; geometry; polygon; mapping
Show Figures

Figure 1

MDPI and ACS Style

Castagno, J.; Atkins, E. Polylidar3D-Fast Polygon Extraction from 3D Data. Sensors 2020, 20, 4819. https://doi.org/10.3390/s20174819

AMA Style

Castagno J, Atkins E. Polylidar3D-Fast Polygon Extraction from 3D Data. Sensors. 2020; 20(17):4819. https://doi.org/10.3390/s20174819

Chicago/Turabian Style

Castagno, Jeremy, and Ella Atkins. 2020. "Polylidar3D-Fast Polygon Extraction from 3D Data" Sensors 20, no. 17: 4819. https://doi.org/10.3390/s20174819

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop