Localization accuracy estimation with application to perception design

Landmark-based localization in dynamic environments poses high demands on the perception system of a mobile robot. The pose estimate generally has to fulfill specific accuracy requirements which might be necessitated by dependent systems, such as behavior planning. Thus, in this contribution we focus on the model-based derivation of perception requirements, i.e. detectable landmark types and minimum detection rates, to enable global localization with a specified upper bound on uncertainty. To this end, we utilize stochastic geometry to accurately capture and explicitly consider characteristics of the dynamic environment (e.g. occlusions), and the perception system (e.g. missed detections). From this point our contributions are twofold: i) We propose an analytical model of upper bounds on localization uncertainty. For continuous pose tracking, the Kalman filter equations for intermittent observations are considered and ii) perception requirements, i.e. minimum detection rates, based on specified upper bounds on pose estimation uncertainty are derived. Monte Carlo simulations are used to demonstrate the performance of the proposed methods.