Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 929-933, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-929-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
AN IMPROVED PROGRESSIVE TRIANGULATION ALGORITHM FOR VEHICLE-BORNE LASER POINT CLOUD
Z. Wei1,2, H. Ma1,2, X. Chen1,2, and L. Liu1 1Chinese Academy of Surveying and Mapping, Beijing 100830, China
2Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
Keywords: Vehicle-borne laser point cloud, Ground filter, TIN, Progressive triangulation, Improvement Abstract. The application of classical progressive triangulation filter algorithm for airborne point cloud is very successful, however, there is a big difference between airborne point cloud and vehicle-borne laser point cloud in spatial distribution, density and other aspects. In this paper, a lot of experiments are carried out to improve the filter algorithm for vehicle-borne laser point cloud, which includes as follows: (1) Establish grid index, such as 0.1 meters, only retain the lowest points, which can greatly reduce the number of suspected ground points, and the filtering efficiency is improved significantly; (2) According to the vehicle-borne height and track line, the road face points can be roughly determined. Then the convolution operation is used to ensure the real road points, which are also the ground points. This method cannot have to relax the filter parameters (which will lead to more non-ground points) and ensure the integrity of the road boundary; (3) A method named as "get more and remove some" is proposed for solving the filtering faults at the tail of every points segment caused by the incline scanning face. After the three steps, the filtering is improved obviously on qualification and processing speed.
Conference paper (PDF, 2003 KB)


Citation: Wei, Z., Ma, H., Chen, X., and Liu, L.: AN IMPROVED PROGRESSIVE TRIANGULATION ALGORITHM FOR VEHICLE-BORNE LASER POINT CLOUD, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 929-933, https://doi.org/10.5194/isprs-archives-XLII-2-W7-929-2017, 2017.

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