Modeling Localization Uncertainty for Enhanced Robustness of Automated Vehicles

This paper presents a novel method
for deriving the functionality of an arbitrary localization system
by utilizing odometry data of the vehicle and comparing the
pose and time differences to respective parameterized probability
distributions learned in advance from error-free mapping drives.
Since the performance of the localization system is positiondependent
the method utilizes a grid map and calculates the best
fitting probability distribution for each cell during the mapping
phase. An evaluation of the proposed failure detection shows that
it is capable of recognizing errors of the localization system.