An Improved Robust Filtering Recursive Algorithm for Stochastic Uncertain Vehicle INS/OD Systems With Anomalous Measurements - Shuang Pan; Chao Wang; Chunlei Zhang; Linping Feng (IEEE Access)
The autonomous vehicular navigation technology has gained more and more attention of researchers. Odometer is the most commonly used auxiliary sensor for autonomous navigation in vehicles due to its stability and high accuracy. However, in poor road conditions, vehicle skidding or slipping will seriously affect the navigation accuracy of INS/OD integrated navigation system. To solve this problem, we propose an improved autonomous vehicular navigation system based on robust filter recursive algorithm for stochastic system with outlier measurements. The main contributions of this study can be summarized as: 1) Incremental odometer measurement model is proposed for INS/OD integrated navigation system to solve the problem of calculation of abnormal odometer information. 2) Robust filter recursive algorithm for stochastic uncertain system with outlier measurements caused by vehicle’s slipping and skidding is proposed to improve the robustness of the INS/OD integrated navigation system. Experimental results demonstrate that the proposed method significantly improves positioning accuracy compared to traditional INS/OD integrated navigation methods, especially under vehicle skidding or slipping conditions.