Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways Sensors (Basel, Switzerland) 18 12 Dec. Map-Relative Localization in Lane-Level Maps for ADAS and Autonomous Driving 2014 IEEE Intelligent Vehicles Symposium Proceedings 2014 49 55 10.1109/IVS.2014.6856428 ![]() Real Time Cooperative Localization for Autonomous Vehicles 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) 1186 91 10.1109/ITSC.2016.7795707 Multisensor Data Fusion: A Review of the State-of-the-Art Information Fusion 14 1 28 44 Jan. Map-Based Precision Vehicle Localization in Urban Environments Robotics: Science and Systems III, MITP 2008 Multi-Sensor Self-Localization Based on Maximally Stable Extremal Regions 2014 IEEE Intelligent Vehicles Symposium Proceedings 2014 555 60 10.1109/IVS.2014.6856413 The test results show that multi-sensor fusion improves the vehicle’s localization compared to GPS/IMU or LiDAR alone. It represented a complicated test environment with dynamic and static objects. This approach was successfully tested on FEV’s Smart Vehicle Demonstrator at FEV’s HQ. The output of this stage is then fused with the object-based localization. In the preliminary stage, the SLAM-based vehicle coordinates are fused with the GPS-based positioning. An Extended Kalman Filter (EKF) is utilized to fuse data from all sensors in two phases. 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. To reduce the uncertainty of vehicle localization in such environments, sensor fusion of LiDAR, Radar, Camera, GPS/IMU, and Odometry sensors is utilized. ![]() Autonomous driving in unstructured environments is a significant challenge due to the inconsistency of important information for localization such as lane markings.
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