Accurate and Resilient GPS-Only Localization With Velocity Constraints

GPS is the most popular sensor for outdoor localization. GPS-only localization is the simplest and initial setting, thus it has been employed in many applications. This paper presents more accurate GPS-only localization with two velocity constraints based on Bayesian filtering. GPS-only localization inherently suffers from ambiguity problems in its state variables due to the limitation of position-only observations. These ambiguities lead to incorrect or diverged state estimates, which are commonly observed in cases of violating assumptions in motion and observation models. Since two proposed velocity constraints can resolve the ambiguity problems, EKF localization with two additional constraints can achieve more accurate localization and demonstrate better recovery from broken state estimates. We quantitatively validated such improvements in localization accuracy and recovery using synthetic data with various GPS trajectories and configurations. Experimentally, the constant velocity model with two velocity constraints exhibited around 25% less position error and 70% less orientation error on average compared to the original constant velocity model. We also qualitatively observed similar results with two real-world datasets. In our experiments with real-world datasets, two velocity constraints successfully resolved state ambiguities after abrupt motion and severely incorrect GPS measurements. Our basic implementation is available at https://github.com/mint-lab/filtering_tutorial.