An RFS theoretic for Bayesian feature-based robotic mapping

Abstract

Estimating a FB map requires the joint propagation of the FB map density encapsulating uncertainty in feature number and location. This chapter addresses the joint propagation of the FB map density and leads to an optimal map estimate in the presence of unknown map size, spurious measurements, feature detection and data association uncertainty. The proposed framework further allows for the joint treatment of error in feature number and location estimates. As a proof of concept, the first-order moment recursion, the PHD filter, is implemented using both simulated and real experimental data. The feasibility of the proposed framework is demonstrated, particularly in situations of high clutter density and large data association ambiguity. This chapter establishes new tools for a more generalised representation of the FB map, which is a fundamental component of the more challenging SLAM problem, to follow in Part II.