Exteroceptive sensors provide absolute information from the surrounding envi-ronment and are a critical aspect of any autonomous navigation algorithm. These measurements are subject to many sources of uncertainty, namely detection and data association uncertainty, spurious measurements, biasses as well as measurement noise. To deal with such uncertainty, probabilistic methods are most widely adopted, espe-cially metric based approaches. These probabilistic environmental representations, for autonomous navigation frameworks with uncertain measurements, can generally be subdivided into two main categories - grid based and feature based. Grid based approaches are popular for robotic exploration, obstacle avoidance and path planning, whereas feature based maps, with their reduced dimensionality, are primarily used for large scale localisation, i.e. SLAM. While researchers commonly distinguish both approaches based on their environmental representations, this thesis examines the fun-damental theoretical aspects of estimation theoretic algorithms for both approaches, with emphasis on the measurement likelihoods used to incorporate measurement un-certainty, and their impact on the resulting stochastic problem.

200 p.