Automatic extraction and classification of pole-like objects from vehicle LiDAR point cloud

doi:10.11947/j.AGCS.2020.20190220

Abstract

Abstract: Aiming at the poor quality of vehicle LiDAR point cloud data and the mutual concealment of various ground objects in urban road scenes, an automatic extraction and classification algorithm for pole-like objects was proposed. Firstly, ground points in point cloud data were removed by improving the mathematical morphology algorithm. According to the morphological characteristics of the pole-like objects, preliminary extraction of pole-like objects was carried out through the longitudinal grid template.Secondly, the extracted suspected pole-like objects were regularized with point cloud data and some noise was removed by statistical analysis. Finally, SVM classification model was trained according to the previously established pole-like object samples to classify the extracted pole-like objects. The experimental results showed that the method could effectively extract the pole-like objects in urban road scenes under the condition of poor data quality, and classified the extracted pole-like objects with high precision.

Key words: mobile LiDAR, pole-like objects, feature extraction, objects classification, SVM classification model

CLC Number:

P237

LI Yongqiang, LI Pengpeng, DONG Yahan, FAN Huilong. Automatic extraction and classification of pole-like objects from vehicle LiDAR point cloud[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 724-735.

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