Density variation-based background filtering algorithm for low-channel roadside lidar data. (February 2023)

• Record Type: Journal Article
• Authors: Lin, Ciyun; Zhang, Hongli; Gong, Bowen; Wu, Dayong; Wang, Yi-Jia
• Is Part Of: Optics & laser technology. Volume 158:Part A(2023)
• Publication Date: 2023-02
• Subjects: Roadside LiDAR -- Background filtering -- Density variation -- Road user passing area; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2022.108852
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: A novel background filtering algorithm is proposed based on density variation for low-channel roadside LiDAR. An automatic and real-time method is presented to filter the background points based on cube point characteristics. The proposed method has higher accuracy and performs better in far range than the state-of-the-art algorithms. Abstract: Light Detection and Range (LiDAR) sensor is considered will be widely deployed in the roadside infrastructure if massive production in the near future, as it can extract High-Resolution Micro-level Traffic Data (HRMTD) which is a cornerstone in Intelligent Transportation Systems (ITS) applications. In the field application, background filtering is the first and foremost step to accelerate HRMTD extraction efficiency and improve extraction precision. In this paper, we proposed a novel background filtering algorithm based on density variation for low-channel roadside LiDAR. First, we segmented the detected area into small cubes and analyzed the character of LiDAR points in the detected area by calculating the density variation of the point cloud in continuous time. Second, we constructed an index to distinguish the road user passing area and removed outliers through the DBSCAN algorithm. Third, we excluded the LiDAR points that were not in the passing area. In the experiments, object points obtained percentage, background points excluded percentage, and effective points percentage were used to evaluate the accuracy of background filtering methods. Compared to the state-of-the-art methods, our algorithm has higher filtering accuracy and can perform well in complex sites in real-time. Besides, the proposed algorithm has the best stability, reflecting that the accuracy of the proposed methods does not decrease significantly as distance increases.