Integrity monitoring of Graph‐SLAM using GPS and fish‐eye camera

Abstract

We propose a Simultaneous Localization and Mapping (SLAM)-based Integrity Monitoring (IM) algorithm using GPS and fish-eye camera to compute the protection levels while accounting for multiple faults in GPS and vision. We perform graph optimization using GPS pseudoranges, pixel intensities, vehicle dynamics, and satellite ephemeris to simultaneously localize the vehicle, GPS satellites, and key image pixels in the world frame. We estimate the fault mode vector by analyzing the temporal correlation across pseudorange residuals and spatial correlation across pixel intensity residuals. To isolate the vision faults, we develop a superpixel-based piecewise random sample consensus. For the estimated fault mode, we compute the protection levels by performing worst-case failure slope analysis on the batch realization of linearized Graph-SLAM formulation. We perform real-world experiments in an alleyway in Stanford, California and a semi-urban area in Champaign, Illinois. We demonstrate higher localization accuracy and tighter protection levels as compared to GPS-only SLAM-based IM.