Consistency of feature-based random-set Monte-Carlo localization
Manuel Stübler, Stephan Reuter, Klaus Dietmayer · 2017
Self-localization is one of the most critical parts in robotics and automated driving. Thus, it is quite essential to have some kind of self-assessment for the respective pose estimate. Therefore, this paper introduces a new online approach to check the consistency of feature-based random-set Monte-Carlo Localization (MCL). The basic idea is to detect inconsistencies of the assumed measurement process in a stochastic manner in order to infer validity of the localization result. This concept is closely linked to the Normalized Innovation Squared (NIS) in Kalman filtering techniques. The problem of checking the consistency online, in absence of ground-truth data, is formulated using confidence intervals for the estimated measurement model parameters. In contrast to the single-object Kalman filter, multi-object filters not only consider the spatial uncertainty of sensor measurements, but also the clutter rate and missed detections. Thus, all those measurement model parameters need to be observed and checked for consistency. The proposed concept is applied to a random-set formulation of MCL that is formally derived in the present contribution. The evaluation is done using real-world data from a test vehicle in a scenario that covers public urban and rural roads.