PL-RAS: A Robust Localization System with Real Time Protection Level Calculation and Adaptive Kernel for Enhanced Integrity

Uncertainty in perception tasks, such as localization, is critical for autonomous systems. Many localization systems fail to ensure that their reported uncertainties encompass the true pose. This paper addresses this issue using the integrity framework. We focus on two main aspects. First, fault-tolerant localization through qualitative evaluation. Second, quantitative estimation of error bounds using (horizontal) protection levels. We introduce PL-RAS (Protection Level-based Robust and Adaptive Solver). This solver aids robustness in non-linear least squares optimization, including factor graph-based localization systems. PL-RAS improves uncertainty awareness and enhances system integrity. It strengthens both qualitative and quantitative integrity aspects. We test the approach on urban road data collected using an acquisition vehicle at Valeo's Créteil VMTC site. The results confirm PL-RAS's effectiveness. In one dataset, the integrity risks are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4.0 \times 10^{-4}$</tex> (lateral) and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$34.0 \times 10^{-3}$</tex> (longitudinal). In a more challenging dataset, the lateral risk becomes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.0 \times 10^{-4}$</tex>, while the longitudinal risk increases to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$92.3\times 10^{-3}$</tex>. These findings demonstrate PL-RAS's robustness in fault tolerance and protection level estimation.