Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization

Applied Sciences

Byeongwoo Kim¹  Ming Lin¹ 

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

关键词: particle-aided unscented Kalman filter;    non-Gaussian;    sensor fusion;    localization;    unscented Kalman filter;   

DOI  :  10.3390/app10155045

来源: DOAJ

【 摘 要 】

The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. However, the previous basic PAUKF only considers the infrastructures in two dimensions (2D). This previous PAUKF 2D limitation rendered it inoperable in the real world, which is full of three-dimensional (3D) features. In this paper, we have extended the previous basic PAUKF’s particle weighting process based on the multivariable normal distribution for handling 3D features. The extended PAUKF also raises the feasibility of fusing multisource perception data into the PAUKF framework. The simulation results show that the extended PAUKF has better real-world applicability than the previous basic PAUKF.

【 授权许可】

Unknown