Environmental information-assisted intelligent fusion localization for vehicles in urban area

Abstract

In this paper, we propose a localization methodology aimed at improving accuracy through two primary aspects: environment information assisting fusion algorithm and the elaborate localization errors prediction. First, the environment-assisted particle filter (EA-PF) is proposed by enhancing the particle filter algorithm with perceived environmental feature information, which is reflected by the rasterized map constructed based on LiDAR sensors. Then, an elaborate localization error prediction model is designed to accurately forecast Inertial Navigation System (INS) errors. The model comprises the noise characteristic feature extraction and encoding network and multi-feature error prediction network. The localization errors in the EA-PF output can be further compensated. The proposed localization methodology can be divided into two operational modes based on the availability of Global Navigation Satellite System (GNSS) signal: GNSS-available and GNSS-unavailable modes. In the GNSS-available mode, the EA-PF algorithm yields precise localization results, while in the GNSS-unavailable modes, the elaborate localization error prediction model can effectively predict INS errors and provide the compensation for the EA-PF. To validate the effectiveness of the proposed methodology, relevant test experiments were performed using the KITTI dataset. The experimental validation results demonstrate the feasibility and effectiveness of the proposed methodology. The experimental results demonstrate that the EA-PF outperforms traditional PF methods by improving the root mean square error (RMS.) by 32.70 % on average during 90 s GNSS outages. Moreover, the proposed localization methodology achieves 2.30 m RMS error on average during 90 s GNSS outage.