Fast-Spherical-Projection-Based Point Cloud Clustering Algorithm

Abstract

Roadside LiDAR (light detection and ranging) is a solution to fill in the gaps for connected vehicles (CV) by detecting the status of global road users at transportation facilities. It relies greatly on the clustering algorithm for accurate and rapid data processing so as to ensure effectiveness and reliability. To contribute to better roadside LiDAR-based transportation facilities, this paper presents a fast-spherical-projection-based clustering algorithm (FSPC) for real-time LiDAR data processing with higher clustering accuracy and noise handling. The FSPC is designed to work on a spherical map which could be directly derived from the instant returns of a LiDAR sensor. A 2D-window searching strategy is specifically designed to accelerate the computation and alleviate the density variation impact in the LiDAR point cloud. The test results show the proposed algorithm can achieve a high processing efficiency with 24.4 ms per frame, satisfying the real-time requirement for most common LiDAR applications (100 ms per frame), and it also ensures a high accuracy in object clustering, with 96%. Additionally, it is observed that the proposed FSPC allows a wider detection range and is more stable, tackling the surge in foreground points that frequently occurs in roadside LiDAR applications. Finally, the generality of the proposed FSPC indicates the proposed algorithm could also be implemented in other areas such as autonomous driving and remote sensing.

Keywords

data and data science automatic vehicle detection and identification systems computer vision data science remote sensing vehicle detection

Get full access to this article

View all access options for this article.

References

Tian

Yuan

Raster-Based Background Filtering for Roadside LiDAR Data. IEEE Access, Vol. 7, 2019, pp. 76779–76788.

Ilgin Guler

Menendez

Meier

Using Connected Vehicle Technology to Improve the Efficiency of Intersections. Transportation Research Part C: Emerging Technologies, Vol. 46, 2014, pp. 121–131. http://doi.org/10.1016/j.trc.2014.05.008.

Zheng

Automatic Background Filtering and Lane Identification with Roadside LiDAR Data. Proc., IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, IEEE, New York, March 15, 2018, pp. 1–6.

Sun

Zhuang

Road Boundary-Enhanced Automatic Background Filtering for Roadside LiDAR Sensors. IEEE Intelligent Transportation Systems Magazine, 2021, pp. 2–14. https://doi.org/10.1109/MITS.2021.3049358.

Zhao

Liu

Zheng

Detection and Tracking of Pedestrians and Vehicles Using Roadside LiDAR Sensors. Transportation Research Part C: Emerging Technologies, Vol. 100, 2019, pp. 68–87. https://doi.org/10.1016/j.trc.2019.01.007.

Chen

Yue

Yuan

Wang

Deer Crossing Road Detection with Roadside LiDAR Sensor. IEEE Access, Vol. 7, 2019, pp. 65944–65954.

Wang

Wen

Sun

An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation. Sensors (Basel, Switzerland), Vol. 19, No. 1, 2019, p. 172.

Zhao

Dong

Liu

Wei

Zhang

Chen

An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings. Mathematical Problems in Engineering, Vol. 2019, 2019. https://doi.org/10.1155/2019/3026758.

Velodyne LiDAR, Inc. VLS-128 User Manual 63-9483. Velodyne LiDAR, Inc., San Jose, CA.

10.

Himmelsbach

Luettel

Wuensche

H. J.

Real-Time Object Classification in 3D Point Clouds Using Point Feature Histograms. Proc., IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, IEEE, New York, 2009, pp. 994–1000.

11.

Suzuki

Horiba

Sugie

Linear-Time Connected-Component Labeling Based on Sequential Local Operations. Computer Vision and Image Understanding, Vol. 89, No. 1, 2003, pp. 1–23.

12.

John

Long

Liu

Mita

Automatic Calibration and Registration of Lidar and Stereo Camera without Calibration Objects. Proc., IEEE International Conference on Vehicular Electronics and Safety (ICVES), Yokohama, Japan, IEEE, New York, 2016, pp. 231–237.

13.

Ravankar

Kobayashi

Ravankar

Emaru

A Connected Component Labeling Algorithm for Sparse Lidar Data Segmentation. Proc., 6th International Conference on Automation, Robotics and Applications (ICARA), Queenstown, New Zealand, IEEE, New York, 2015, pp. 437–442.

14.

Börcs

Nagy

Benedek

Fast 3-D Urban Object Detection on Streaming Point Clouds. In Computer Vision—ECCV 2014 Workshops, Vol. 8926. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ( Agapito

Bronstein

Rother

, eds), Springer, Cham, Switzerland, 2015, pp. 628–639.

15.

Zermas

Izzat

Papanikolopoulos

Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications. Proc., IEEE International Conference on Robotics and Automation (ICRA), Singapore, IEEE, New York, 2017, pp. 5067–5073.

16.

Bogoslavskyi

Stachniss

Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation. Proc., IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea (South), IEEE, New York, 2016, pp. 163–169.

17.

Ren

Gao

Zhao

Yao

Chao

The Connected-Component Labeling Problem: A Review of State-of-the-Art Algorithms. Pattern Recognition, Vol. 70, 2017, pp. 25–43.

18.

Chao

Suzuki

A Run-Based Two-Scan Labeling Algorithm. IEEE Transactions on Image Processing, Vol. 17, No. 5, 2008, pp. 749–756.

19.

Hasecke

Hahn

Kummert

FLIC : Fast Lidar Image Clustering. arXiv Preprint arXiv:2003.00575, 2020.