Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter

Sensors

Byeongwoo Kim¹  Ming Lin¹  Jaewoo Yoon¹ 

[1] Department of Electrical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 44610, Korea;

关键词: particle filter;    sensor fusion;    self-driving car;    unscented Kalman filter;    vehicle model;    Monte Carlo localization;   

DOI  :  10.3390/s20092544

来源: DOAJ

【 摘 要 】

Localization is one of the key components in the operation of self-driving cars. Owing to the noisy global positioning system (GPS) signal and multipath routing in urban environments, a novel, practical approach is needed. In this study, a sensor fusion approach for self-driving cars was developed. To localize the vehicle position, we propose a particle-aided unscented Kalman filter (PAUKF) algorithm. The unscented Kalman filter updates the vehicle state, which includes the vehicle motion model and non-Gaussian noise affection. The particle filter provides additional updated position measurement information based on an onboard sensor and a high definition (HD) map. The simulations showed that our method achieves better precision and comparable stability in localization performance compared to previous approaches.

【 授权许可】

Unknown