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Open AccessArticle

A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles

1
Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore
2
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(9), 2140; https://doi.org/10.3390/s17092140
Received: 25 August 2017 / Revised: 14 September 2017 / Accepted: 15 September 2017 / Published: 18 September 2017
Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization. View Full-Text
Keywords: sensor fusion; Unscented Kalman Filter (UKF); vehicle localization sensor fusion; Unscented Kalman Filter (UKF); vehicle localization
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Meng, X.; Wang, H.; Liu, B. A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles. Sensors 2017, 17, 2140.

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