Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

In this paper, we propose a GNSS/IMU localization system for mobile robots when wheel speed sensors cannot be attached. Highly accurate location infor-mation is required for autonomous navigation of mo-bile robots. A typical method of acquiring location information is to use a Kalman filter for position es-timation. The Kalman filter is a maximum-likelihood estimation method that assumes normally distributed noise. However, non-normally distributed GNSS mul-tipath noise that frequently occurs in urban environ-ments causes the Kalman filter to break down, and de-grades the estimation performance. Other GNSS/IMU localization methods capable of lane-level estimation in urban environments use wheel speed sensors, which are unsuitable for the present situation. In this study, we aim to improve the performance of lane-level local-ization by adding a vehicle speed estimation function to adapt the method to those requiring wheel speed sensors. The proposed method optimizes time-series data to accurately compensate for accelerometer bias errors and reduce GNSS multipath noise. The eval-uation confirmed the effectiveness of the proposed method, with improved velocity and position estima-tion performance compared with the Kalman filter method.