  This paper mainly studies the localization and mapping of range sensing
robots in the confidence-rich map (CRM) and then extends it to provide a full
state estimate for information-theoretic exploration. Most previous works about
active simultaneous localization and mapping and exploration always assumed the
known robot poses or utilized inaccurate information metrics to approximate
pose uncertainty, resulting in imbalanced exploration performance and
efficiency in the unknown environment. This inspires us to extend the
confidence-rich mutual information (CRMI) with measurable pose uncertainty.
Specifically, we propose a Rao-Blackwellized particle filter-based localization
and mapping scheme (RBPF-CLAM) for CRM, then we develop a new closed-form
weighting method to improve the localization accuracy without scan matching. We
further derive the uncertain CRMI (UCRMI) with the weighted particles by a more
accurate approximation. Simulations and experimental evaluations show the
localization accuracy and exploration performance of the proposed methods.
