Robust Statistics vs. Machine Learning vs. Bayesian Inference: Insights into Handling Faulty GNSS Measurements in Field Robotics

This paper presents research findings on handling faulty measurements (i.e., outliers) of global navigation satellite systems (GNSS) for vehicle localization under adverse signal conditions in field applications, where raw GNSS data are frequently corrupted due to environmental interference such as multipath, signal blockage, or non-line-of-sight conditions. In this context, we investigate three strategies applied specifically to GNSS pseudorange observations: robust statistics for error mitigation, machine learning for faulty measurement prediction, and Bayesian inference for noise distribution approximation. Since previous studies have provided limited insight into the theoretical foundations and practical evaluations of these three methodologies within a unified problem statement (i.e., state estimation using ranging sensors), we conduct extensive experiments using real-world sensor data collected in diverse urban environments. Our goal is to examine both established techniques and newly proposed methods, thereby advancing the understanding of how to handle faulty range measurements, such as GNSS, for robust, long-term vehicle localization. In addition to presenting successful results, this work highlights critical observations and open questions to motivate future research in robust state estimation.