Vehicle state estimation by moving horizon estimation considering occlusion and outlier on 3D static cameras

Measurement using 3D static cameras can achieve high accuracy localization, but outlier or occlusion should be considered. It is necessary to compensate them to improve accuracy. To address this issue, we introduce Moving Horizon Estimation (MHE) and compare it with Extended Kalman Filter (EKF) to evaluate the estimation accuracy. In this paper, we conduct 3D static camera measurement for a vehicle under challenging conditions in both numerical simulation and experiment. Then, through estimation of position and heading angle of the vehicle, the estimation accuracy is compared to show the effectiveness of the state estimation by MHE even under occlusion of images.