Hostname: page-component-669899f699-b58lm Total loading time: 0 Render date: 2025-04-29T21:54:08.997Z Has data issue: false hasContentIssue false
  • English
  • Français

An efficient LiDAR-based localization method for self-driving cars in dynamic environments

Published online by Cambridge University Press:  20 April 2021

Yihuan Zhang Open the ORCID record for Yihuan Zhang [Opens in a new window] ,
Liang Wang ,
Xuhui Jiang ,
Yong Zeng  and
Yifan Dai
Yihuan Zhang*
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Liang Wang
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Xuhui Jiang
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Yong Zeng
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
Yifan Dai
Affiliation:
Intelligent Connected Vehicle Center, Tsinghua Automotive Research Institute, Suzhou, China
*
*Corresponding author. Email: zhangyihuan@tsari.tsinghua.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

Keywords

Self-driving carsCurb detectionMap matchingReal-time localization
Type
Article
Information
Robotica , Volume 40 , Issue 1 , January 2022 , pp. 38 - 55
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Knaup, J. and Homeier, K., “Graph-based Environmental Modelling and Function Independent Situation Analysis for Driver Assistance Systems,” Proceedings of the IEEE International Conference on Intelligent Transportation Systems (2010) pp. 428–432.Google Scholar
Levinson, J., Montemerlo, M. and Thrun, S., “Map-based precision vehicle localization in urban environments,” Rob. Sci. Syst. 4(2007), 1–6 (2007).Google Scholar
Maddern, W. and Newman, P., “Real-Time Probabilistic Fusion of Sparse 3D Lidar and Dense Stereo,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016) pp. 2181–2188.Google Scholar
Elias, X., Paraskevi, Z. and Andreas, N., “Path planning and scheduling for a fleet of autonomous vehicles,” Robotica 34(10), 2257–2267 (2016).Google Scholar
Gruyer, D., Belaroussi, R. and Revilloud, M., “Map-Aided Localization with Lateral Perception,” Proceedings of IEEE Intelligent Vehicles Symposium (2014) pp. 674–680.Google Scholar
Mattern, N. and Wanielik, G., “Camera-based Vehicle Localization at Intersections Using Detailed Digital Maps,” Proceedings of IEEE/ION Position, Location and Navigation Symposium (2010) pp. 1100–1107.Google Scholar
Schreiber, M., Knöppel, C. and Franke, U., “Laneloc: Lane Marking Based Localization Using Highly Accurate Maps,” Proceedings of IEEE Intelligent Vehicles Symposium (2013) pp. 449–454.Google Scholar
Cui, D., Xue, J. and Zheng, N., “Real-time global localization of robotic cars in lane level via lane marking detection and shape registration,” IEEE Trans. Intell. Transp. Syst. 17(4), 1039–1050 (2016).CrossRefGoogle Scholar
Tao, Z., Bonnifait, P., Fremont, V. and Ibanez-Guzman, J., “Lane Marking Aided Vehicle Localization,” Proceedings of the IEEE International Conference on Intelligent Transportation Systems (2013) pp. 1509–1515.Google Scholar
Llamazares, Á., Molinos, E. and Ocaña, M., “Detection and tracking of moving obstacles (DATMO): A review,” Robotica 38(5), 761–774 (2020).CrossRefGoogle Scholar
Giulio, R., Annalisa, M. and Rainer, W., “LIDAR and stereo combination for traversability assessment of off-road robotic vehicles,” Robotica 34(12), 2823–2833 (2016).Google Scholar
Schlichting, A. and Brenner, C., “Localization Using Automotive Laser Scanners and Local Pattern Matching,” Proceedings of IEEE Intelligent Vehicles Symposium (2014) pp. 414–419.Google Scholar
Kim, D., Chung, T. and Yi, K., “Lane Map Building and Localization for Automated Driving Using 2D Laser Rangefinder,” Proceedings of IEEE Intelligent Vehicles Symposium (2015) pp. 680–685.Google Scholar
Matthaei, R., Bagschik, G. and Maurer, M., “Map-Relative Localization in Lane-Level Maps for ADAS and Autonomous Driving,” Proceedings of IEEE Intelligent Vehicles Symposium (2014) pp. 49–55.Google Scholar
Hata, A., Osorio, F. and Wolf, D., “Robust Curb Detection and Vehicle Localization in Urban Environments,” Proceedings of IEEE Intelligent Vehicles Symposium (2014) pp. 1257–1262.Google Scholar
Schindler, A., “Vehicle Self-localization with High-Precision Digital Maps,” Proceedings of IEEE Intelligent Vehicles Symposium (2013) pp. 141–146.Google Scholar
Jo, K., Jo, Y., Suhr, J., Jung, H. and Sunwoo, M., “Precise localization of an autonomous car based on probabilistic noise models of road surface marker features using multiple cameras,” IEEE Trans. Intell. Transp. Syst. 16(6), 3377–3392 (2015).CrossRefGoogle Scholar
Lu, W., Zhou, Y., Wan, G., Hou, S. and Song, S., “L3-Net: Towards Learning Based Lidar Localization for Autonomous Driving,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019) pp. 6389–6398.Google Scholar
Yin, H., Tang, L., Ding, X., Wang, Y. and Xiong, R., “LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles,” Proceedings of IEEE Intelligent Vehicles Symposium (2018) pp. 728–733.Google Scholar
Chen, H., Liu, C. and Chen, Q., “Self-localization in highly dynamic environments based on dual-channel unscented particle filter,” Robotica, 1–14 (2020). doi: 10.1017/S0263574720001046.CrossRefGoogle Scholar
Sofia, Y. and Muhammad Bilal, K., “Information fusion of GPS, INS and odometer sensors for improving localization accuracy of mobile robots in indoor and outdoor applications,” Robotica 39(2), 250–276 (2021).Google Scholar
Levinson, J. and Thrun, S., “Robust Vehicle Localization in Urban Environments Using Probabilistic Maps,” Proceedings of IEEE Robotics and Automation (2010) pp. 4372–4378.Google Scholar
Wolcott, R. and Eustice, R., “Visual Localization Within Lidar Maps for Automated Urban Driving,” Proceedings of IEEE/RSJ Intelligent Robots and Systems (2014) pp. 176–183.Google Scholar
Baldwin, I. and Newman, P., “Laser-Only Road-Vehicle Localization with Dual 2D Push-Broom LIDARS and 3D Priors,” Proceedings of IEEE/RSJ Intelligent Robots and Systems (2012) pp. 2490–2497.Google Scholar
Yoneda, K., Tehrani, H., Ogawa, T., Hukuyama, N. and Mita, S., “Lidar Scan Feature for Localization with Highly Precise 3-D Map,” Proceedings of IEEE Intelligent Vehicles Symposium (2014) pp. 1345–1350.Google Scholar
Yoneda, K., Yang, C., Mita, S., Okuya, T. and Muto, K., “Urban Road Localization by Using Multiple Layer Map Matching and Line Segment Matching,” Proceedings of IEEE Intelligent Vehicles Symposium (2015) pp. 525–530.Google Scholar
Thrun, S., “Probabilistic Robotics,” Commun. ACM 45(3), 52–57 (2006).CrossRefGoogle Scholar
Pfaff, P., Plagemann, C. and Burgard, W., “Gaussian Mixture Models for Probabilistic Localization,” Proceedings of IEEE Robotics and Automation (2008) pp. 467–472.Google Scholar
Oberlander, J., Roennau, A. and Dillmann, R., “Hierarchical SLAM Using Spectral Submap Matching with Opportunities for Long-Term Operation,” Proceedings of the 16th Advanced Robotics (2013) pp. 1–7.Google Scholar
Wurm, K., Stachniss, C. and Grisetti, G., “Bridging the gap between feature- and grid-based SLAM,” Rob. Auton. Syst. 58(2), 140–148 (2010).CrossRefGoogle Scholar
Zinoune, C., Bonnifait, P. and Ibañez-Guzmán, J., “Sequential FDIA for autonomous integrity monitoring of navigation maps on board vehicles,” IEEE Trans. Intell. Transp. Syst. 17(1), 143–155 (2015).CrossRefGoogle Scholar
Fujii, A., Tanaka, M., Yabushita, H. Mori, T. and Odashima, T., “Detection of Localization Failure Using Logistic Regression,” Proceedings of IEEE/RSJ Intelligent Robots and Systems (2015) pp. 4313–4318.Google Scholar
Kaneko, S., Kondo, T. and Miyamoto, A., “Robust matching of 3D contours using iterative closest point algorithm improved by M-estimation,” Pattern Recogn. 36(9), 2041–2047 (2003).CrossRefGoogle Scholar
Pink, O., “Visual Map Matching and Localization Using a Global Feature Map,” Proceedings of Computer Vision and Pattern Recognition Workshops (2008) pp. 1–7.Google Scholar
Oberlander, J., Roennau, A. and Dillmann, R., “Hierarchical SLAM Using Spectral Submap Matching with Opportunities for Long-Term Operation,” Proceedings of the 16th Advanced Robotics (2013) pp. 1–7.Google Scholar
Rohde, J., Völz, B., Mielenz, H. and Zöllner, J., “Precise Vehicle Localization in Dense Urban Environments,” Proceedings of the 19th IEEE Intelligent Transportation Systems (2016) pp. 853–858.Google Scholar
“Velodyne HDL 32-E LiDAR,” http://www.velodynelidar.com/lidar/hdlproducts/hdl32e.aspx.Google Scholar
Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X. and Li, H. “PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020) pp. 10529–10538.Google Scholar
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L. and Bennamoun, M. “Deep learning for 3D point clouds: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. (2020). doi: 10.1109/TPAMI.2020.3005434.CrossRefGoogle Scholar
Yang, Z., Sun, Y., Liu, S. and Jia, J. “3DSSD: Point-based 3D Single Stage Object Detector,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020) pp. 10529–10538.Google Scholar
Zhang, Y., Wang, J., Wang, X., Li, C. and Wang, L., “A Real-Time Curb Detection and Tracking Method for UGVs by Using a 3D-LIDAR Sensor,” Proceedings of IEEE Conference on Control Applications (2015) pp. 1020–1025.Google Scholar
Besl, P. and McKay, N., “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992).CrossRefGoogle Scholar
Arun, K., Huang, T. and Blostein, S., “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698–700 (1987).CrossRefGoogle ScholarPubMed
Zhang, Y., Wang, L., Wang, J. and Dolan, J. “Real-Time Localization Method for Autonomous Vehicle Using 3D-LIDAR,” International Symposium on Dynamics of Vehicles on Roads and Tracks (2017) pp. 614–619.Google Scholar