Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements
This work improves navigation accuracy for autonomous ground vehicles, particularly during low-dynamic motion, but the improvement is incremental.
The authors propose using past GNSS measurements with a motion model to extract vehicle acceleration, which is then integrated into an INS/GNSS filter. On two real-world datasets, this approach improved mean position RMSE by 11.40% and 20.74% compared to standard position-aided filters.
Accurate and reliable navigation is essential for autonomous ground vehicle operations. Standard INS/GNSS fusion relies on GNSS position updates, which provide limited observability of orientation and inertial sensor error states, particularly during low-dynamic motion. In this work, we propose utilizing past GNSS measurements alongside a motion model to extract meaningful vehicle acceleration information. This acceleration measurement is then integrated into the INS/GNSS filter to improve its robustness and accuracy. The proposed approach is evaluated on two real-world unmanned ground vehicle datasets collected from different mobile platforms and inertial sensor grades. Results demonstrate consistent positioning accuracy improvements relative to the standard position-aided filter, with mean position root mean square error improvements of 11.40 % and 20.74 % on the two datasets, respectively.