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Autonomous Vehicle Multi-Sensors Localization in Unstructured Environment
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Autonomous driving in unstructured environments is a significant challenge due to the inconsistency of important information for localization such as lane markings. To reduce the uncertainty of vehicle localization in such environments, sensor fusion of LiDAR, Radar, Camera, GPS/IMU, and Odometry sensors is utilized. This paper discusses a hybrid localization technique developed using: LiDAR-based Simultaneous Localization and Mapping (SLAM), GPS/IMU, Odometry data, and object lists from Radar, LiDAR, and Camera sensors. An Extended Kalman Filter (EKF) is utilized to fuse data from all sensors in two phases. In the preliminary stage, the SLAM-based vehicle coordinates are fused with the GPS-based positioning. The output of this stage is then fused with the object-based localization. This approach was successfully tested on FEV’s Smart Vehicle Demonstrator at FEV’s HQ. It represented a complicated test environment with dynamic and static objects. The test results show that multi-sensor fusion improves the vehicle’s localization compared to GPS/IMU or LiDAR alone.
CitationAlrousan, Q., Alzu'bi, H., Pfeil, A., and Tasky, T., "Autonomous Vehicle Multi-Sensors Localization in Unstructured Environment," SAE Technical Paper 2020-01-1029, 2020, https://doi.org/10.4271/2020-01-1029.
Data Sets - Support Documents
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- Deusch, H. et al , “Multi-Sensor Self-Localization Based on Maximally Stable Extremal Regions,” in 2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014, 555-60, IEEE, 10.1109/IVS.2014.6856413.
- Burgard, W., Brock, O., and Stachniss, C. , “Map-Based Precision Vehicle Localization in Urban Environments,” Robotics: Science and Systems III, MITP, 2008.
- Khaleghi, B. et al. , “Multisensor Data Fusion: A Review of the State-of-the-Art,” Information Fusion 14(1):28-44, Jan. 2013, doi:10.1016/j.inffus.2011.08.001.
- Bounini, F. et al. “Real Time Cooperative Localization for Autonomous Vehicles,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016, 1186-91, IEEE, 10.1109/ITSC.2016.7795707.
- Matthaei, R., et al , “Map-Relative Localization in Lane-Level Maps for ADAS and Autonomous Driving,” in 2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014, 49-55, IEEE, 10.1109/IVS.2014.6856428.
- Jang, E.S. et al. , “Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways,” Sensors (Basel, Switzerland) 18(12), Dec. 2018, doi:10.3390/s18124389.
- Kloeden, H. et al. , “Vehicle Localization Using Cooperative RF-Based Landmarks,” in 2011 IEEE Intelligent Vehicles Symposium (IV),2011, 387-92, IEEE , 10.1109/IVS.2011.5940474.
- Rohani, M. et al. , “A New Decentralized Bayesian Approach for Cooperative Vehicle Localization Based on Fusion of GPS and Inter-Vehicle Distance Measurements,” in 2013 International Conference on Connected Vehicles and Expo (ICCVE), 2013, 473-79, IEEE, 10.1109/ICCVE.2013.6799839.
- Ghallabi, F. et al. , “LIDAR-Based Lane Marking Detection For Vehicle Positioning in an HD Map,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Vol. 2018, IEEE, 2018, 2209-14, 10.1109/ITSC.2018.8569951.
- Suhr, J.K. et al. , “Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments,” IEEE Transactions on Intelligent Transportation Systems 18(5):1078-1086, May 2017, doi:10.1109/TITS.2016.2595618.
- Zhang, J. and Singh, S. , “LOAM: Lidar Odometry and Mapping in Real-Time,” Robotics: Science and Systems 2, 2014.
- Kotilainen, I., Händel, C., Hamid, A., Zakir, U., Nykänen, L., Santamala, H., Schirokoff, A., Autioniemi, M., Öörni, R., and Fieandt, N. , “Arctic Challenge Project’s Final Report: Road Transport Automation in Snowy and Icy Conditions,” 2019.