Temporal landmark validation in CML

Current techniques to concurrent map building and localization (CML) have been devised for static environments, and lack robustness in more realistic situations. In this communication we provide new ideas that extend the typical stochastic estimation approach to CML, to take into account the dynamics of the environment. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association. The incorporation of a novel temporal landmark quality test, together with the spatial compatibility tests already available, help alleviate the difficulty of data association. We propose a pair of temporal landmark quality functions to aid in those situations in which landmark observations might not be consistent in time; and show how by incorporating these functions, the overall estimation-theoretic approach to CML is improved. Special attention is paid in that the removal of landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature. Namely, asymptotic convergence and full correlation.