Consistent outdoor vehicle localization by bounded-error state estimation

Abstract

Localization is a part of many automotive applications where safety is of crucial importance. We think that the best way to guarantee the safety in these applications is to guarantee the results of their embedded localization algorithms. Unfortunately localization of vehicles is mostly solved by Bayesian methods which (due to their structure) can only guarantee their results in a probabilistic way. Interval analysis allows an alternative approach with bounded-error state estimation. Such an approach provides a bounded set of configurations that is guaranteed to surround the actual vehicle configuration. We have validated the bounded-error state estimation with an outdoor vehicle equipped with odometers, a GPS receiver and a gyro. With the experimental results we compare the bounded-error state estimation with the particle filter localization in terms of consistency and computation time.