This paper presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets  . Polylidar is able to extract multiple disjoint polygons and capture interior holes. The algorithm begins by triangulating the point set and filtering triangles by user configurable parameters such as triangle edge length. Next, connected triangles are extracted into triangular meshes representing the shape of the point set. The key to Polylidar's speed, and the main contribution of this paper, is in efficiently transforming each triangular mesh into a concave polygon . This paper describes Polylidar and provides benchmarks to comparatively evaluate its speed and accuracy. Results show good accuracy and \sim 4 times speedup compared to other concave polygon extraction methods.
This paper presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets , including interior holes. Plane segmented point clouds can be input into Polylidar to extract their polygonal counterpart, thereby reducing map size and improving visualization . The algorithm begins by triangulating the point set and filtering triangles by user configurable parameters such as triangle edge length. Next, connected triangles are extracted into triangular mesh regions representing the shape of the point set. Finally each region is converted to a polygon through a novel boundary following method which accounts for holes. Real-world and synthetic benchmarks are presented to comparatively evaluate Polylidar speed and accuracy. Results show comparable accuracy and more than four times speedup compared to other concave polygon extraction methods.
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, including interior holes. Plane segmented point clouds can be input into Polylidar to extract their polygonal counterpart, thereby reducing map size and improving visualization
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Polylidar is able to extract multiple disjoint polygons and capture interior holes.
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meshes
mesh regions
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The key to Polylidar's speed, and the main contribution of this paper, is in efficiently transforming each triangular mesh into a concave polygon . This paper describes Polylidar and provides benchmarks
Finally each region is converted to a polygon through a novel boundary following method which accounts for holes. Real-world and synthetic benchmarks are presented
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its
Polylidar
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good accuracy and \sim 4
comparable accuracy and more than four