The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 693–698, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-693-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 693–698, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-693-2016

  16 Jun 2016

16 Jun 2016

A FAST AND ROBUST ALGORITHM FOR ROAD EDGES EXTRACTION FROM LIDAR DATA

Kaijin Qiu, Kai Sun, Kou Ding, and Zhen Shu Kaijin Qiu et al.
  • Leador Spatial Information Technology Co., Ltd. Building No.12, HUST Scien &Tech Park, East Lake Hi-Tech Zone, Wuhan, China

Keywords: Mobile Mapping Systems (MMS), LiDAR data, Point clouds, Road edges extraction, Rough plane, Refined plane

Abstract. Fast mapping of roads plays an important role in many geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance. How to extract various road edges fast and robustly is a challenging task. In this paper, we present a fast and robust algorithm for the automatic road edges extraction from terrestrial mobile LiDAR data. The algorithm is based on a key observation: most roads around edges have difference in elevation and road edges with pavement are seen in two different planes. In our algorithm, we firstly extract a rough plane based on RANSAC algorithm, and then multiple refined planes which only contains pavement are extracted from the rough plane. The road edges are extracted based on these refined planes. In practice, there is a serious problem that the rough and refined planes usually extracted badly due to rough roads and different density of point cloud. To eliminate the influence of rough roads, the technology which is similar with the difference of DSM (digital surface model) and DTM (digital terrain model) is used, and we also propose a method which adjust the point clouds to a similar density to eliminate the influence of different density. Experiments show the validities of the proposed method with multiple datasets (e.g. urban road, highway, and some rural road). We use the same parameters through the experiments and our algorithm can achieve real-time processing speeds.