Particle-Filter-Based LiDAR Localization for Indoor Parking Lot
Source abstract
Abstract
Accurate localization of autonomous vehicles in indoor environments is challenging due to the absence of GPS signals, so various studies have explored the use of environmental sensors to address this limitation. In this paper, we propose an indoor localization algorithm that utilizes a 3D LiDAR sensor and a 2D map, supported by improved motion and sensor modeling tailored for indoor parking lots. These environments contain complex conditions in which static noise from parked vehicles and dynamic noise from moving vehicles coexist, requiring a localization method capable of maintaining robustness under high-noise conditions. In this study, vehicle odometry was obtained using LOAM-style scan-to-scan LiDAR odometry, and a particle filter was implemented based on this information. The proposed algorithm was validated using a test vehicle in two indoor parking lots under three different conditions: when the lot was empty, when parked vehicles were present, and when other moving vehicles were present. Experimental results demonstrated that the algorithm achieved an average localization error of approximately 0.09 m across all scenarios, confirming its effectiveness for indoor parking environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Indoor and Outdoor Localization Technologies · Smart Parking Systems Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronics Engineering, Korea National University of Transportation, Chungju-si 27469, Republic of Korea
Funding
Ministry of Trade, Industry and Energy · Ministry of Education
References (27)
Show source-provided references
1. Mapping and Localization from Planar Markers10.1016/j.patcog.2017.08.010 ↗
2. Wang, Y.-T., Peng, C.-C., Ravankar, A.A., and Ravankar, A. (2018). A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm. Sensors, 18.10.3390/s18041294 ↗
3. Xin, R., Guo, N., Ma, X., Lu, G., and Feng, Z. (2024). RLI-SLAM: Fast Robust Ranging-LiDAR-Inertial Tightly-Coupled Localization and Mapping. Sensors, 24.10.3390/s24175672 ↗
4. Holmberg, M., Karlsson, O., and Tulldahl, M. (2022, January 19–24). LiDAR Positioning for Indoor Precision Navigation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, New Orleans, LA, USA.10.1109/CVPRW56347.2022.00051 ↗
5. Zhang, J., and Singh, S. (2014, January 12–16). LOAM: Lidar Odometry and Mapping in Real-Time. Proceedings of the Robotics: Science and Systems (RSS), Berkeley, CA, USA.10.15607/RSS.2014.X.007 ↗
6. Nimura, M., Kanai, K., and Katto, J. (2023, January 6–8). Accuracy Evaluations of Real-Time LiDAR-Based Indoor Localization System. Proceedings of the IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA.10.1109/ICCE56470.2023.10043573 ↗
7. Liu, Y., Wang, C., Wu, H., Wei, Y., Ren, M., and Zhao, C. (2022). Improved LiDAR Localization Method for Mobile Robots Based on Multi-Sensing. Remote Sens., 14.10.3390/rs14236133 ↗
8. Zhou, G., Xu, S., Zhang, S., Wang, Y., and Xiang, C. (2022). Multi-Floor Indoor Localization Based on Multi-Modal Sensors. Sensors, 22.10.3390/s22114162 ↗
9. Wang, Y., Li, Z., and Huang, T. (June, January 30). Real-Time, Environmentally-Robust 3D LiDAR Localization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China.
10. Robust Lifelong Indoor LiDAR Localization Using the Area Graph10.1109/LRA.2023.3334158 ↗
11. Indoor INS/LiDAR-Based Robot Localization with Improved Robustness Using Cascaded FIR Filter10.1109/ACCESS.2019.2903435 ↗
12. Song, S., Kim, Y., and Lee, J. (June, January 30). Floorplan-Based Localization and Map Update Using LiDAR Sensor. Proceedings of the IEEE International Conference on Robotics and Automation, Xi’an, China.10.1109/UR52253.2021.9494685 ↗
13. Boniardi, F., Caselitz, T., Kümmerle, R., and Burgard, W. (2017, January 24–28). Robust LiDAR-Based Localization in Architectural Floor Plans. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.10.1109/IROS.2017.8206168 ↗
14. Andradi, H., Blumenthal, S., Prassler, E., and Plöger, P.G. (June, January 29). LiDAR-Based Indoor Localization with Optimal Particle Filters Using Surface Normal Constraints. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK.10.1109/ICRA48891.2023.10160274 ↗
15. Lu, W., Wan, G., Hou, S., Zheng, X., and Song, S. (2019, January 15–20). L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00655 ↗
16. Fallon, M.F., Johannsson, H., and Leonard, J.J. (2012, January 14–18). Efficient Scene Simulation for Robust Monte Carlo Localization Using an RGB-D Camera. Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN, USA.10.1109/ICRA.2012.6224951 ↗
17. Pfaff, P., Burgard, W., and Fox, D. (2006). Robust Monte-Carlo Localization Using Adaptive Likelihood Models. European Robotics Symposium 2006, Proceedings of the 1st European Robotics Symposium (EUROS 2006), Palermo, Italy, 16–18 March 2006, Springer.10.1007/11681120_15 ↗
18. Zuo, C., Xie, D., Wu, L., Tang, X., and Zhang, H. (2025). An Improved Adaptive Monte Carlo Localization Algorithm Integrated with a Virtual Motion Model. Sensors, 25.10.3390/s25082471 ↗
19. de Miguel, M.Á., García, F., and Armingol, J.M. (2020). Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS. Sensors, 20.10.3390/s20113145 ↗
20. Bayesian Localization in Real-Time Using Probabilistic Maps and Unscented-Kalman-Filters10.36909/jer.11073 ↗
21. Koide, K., Oishi, S., Yokozuka, M., and Banno, A. (2024, January 13–17). MegaParticles: Range-Based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.10.1109/ICRA57147.2024.10610447 ↗
22. ROS-Based Localization of a Race Vehicle at High-Speed Using LiDAR10.1051/e3sconf/20199504002 ↗
23. Lim, T.Y., Ghignone, E., Baumann, N., and Magno, M. (2024). Robustness Evaluation of Localization Techniques for Autonomous Racing. arXiv.10.23919/DATE58400.2024.10546662 ↗
24. (2024, May 12). loam_velodyne Package. Available online: https://wiki.ros.org/loam_velodyne.
25. (2024, May 12). amcl Package. Available online: https://wiki.ros.org/amcl.
26. Adapting the Sample Size in Particle Filters Through KLD-Sampling10.1177/0278364903022012001 ↗
27. Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics, MIT Press. [1st ed.].