Fault Modes and Methods to Evaluate Integrity Risk for FastSLAM-Based Navigation

Abstract

The fast simultaneous localisation and mapping (FastSLAM), utilising the Rao-Blackwellised particle filter, provides a robust navigation solution in urban environments. Ensuring the integrity of FastSLAM is critical for the safety of autonomous driving applications. Our previous work proposed an empirical integrity risk evaluation method for nominal conditions and a probabilistic bound using PAC (probably approximately correct)–Bayesian theory. However, it was limited by overly conservative risk estimates and a lack of consideration for fault conditions. This study introduces a refined integrity evaluation framework with three main contributions. First, a modified weighting and resampling technique is proposed to reduce conservatism in empirical risk without compromising estimation accuracy. Second, a fault monitoring method is introduced to detect and isolate control input faults during the dynamic update step. Third, a conservative integrity risk evaluation approach is developed for FastSLAM to account for data association faults using probabilistic modelling. Simulation results show that the proposed methods significantly improve integrity performance under both nominal and faulted scenarios.