Rapid Localization and Extraction of Street Light Poles in Mobile LiDAR Point Clouds: A Supervoxel-Based Approach
Rapid Localization and Extraction of Street Light Poles in Mobile LiDAR Point Clouds: A Supervoxel-Based Approach
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DOI：
10.1109/tits.2016.2565698
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发表时间：
2017-02
影响因子：
8.5
通讯作者：
Fan Wu;Chenglu Wen;Yulan Guo;Jingjing Wang;Yongtao Yu;Cheng Wang;Jonathan Li
中科院分区：
文献类型：
--
作者：
Fan Wu;Chenglu Wen;Yulan Guo;Jingjing Wang;Yongtao Yu;Cheng Wang;Jonathan Li
本文提出了一种基于超体素的方法，用于自动定位和提取路灯杆点云采集的移动的激光雷达系统。该方法包括预处理、定位、分割、特征提取和分类五个步骤。首先，将原始点云沿轨迹沿着划分为多个段，去除地面点，并将剩余点分割为超体素。然后，提出了一种鲁棒的定位方法，以准确地识别杆状物体。接下来，提出了一种定位引导分割方法来获得杆状物体。随后，使用支持向量机和随机森林对极点特征进行分类。该方法在三个数据集上进行了评估，其中包括1，055个路灯杆和7.01亿个点。实验结果表明，我们的定位方法取得了98.8%的平均召回值。实验结果表明，该方法比现有的路灯杆定位和提取方法具有更高的鲁棒性和效率。
This paper presents a supervoxel-based approach for automated localization and extraction of street light poles in point clouds acquired by a mobile LiDAR system. The method consists of five steps: preprocessing, localization, segmentation, feature extraction, and classification. First, the raw point clouds are divided into segments along the trajectory, the ground points are removed, and the remaining points are segmented into supervoxels. Then, a robust localization method is proposed to accurately identify the pole-like objects. Next, a localization-guided segmentation method is proposed to obtain pole-like objects. Subsequently, the pole features are classified using the support vector machine and random forests. The proposed approach was evaluated on three datasets with 1,055 street light poles and 701 million points. Experimental results show that our localization method achieved an average recall value of 98.8%. A comparative study proved that our method is more robust and efficient than other existing methods for localization and extraction of street light poles.