Incorporation of Delayed Decision Making into Stochastic Mapping.

This paper presents a technique for incorporating delayed decision making into stochastic mapping algorithms for concurrent mapping and localization. The approach explicitly tracks the error correlations between current and previous vehicle states, enabling the initialization of map features using data from multiple time steps and improved data association decision-making. The method is illustrated using data from a ring of Polaroid sonar sensors from a B21 mobile robot, demonstrating the ability to perform CML with sparse and ambiguous data. [ABSTRACT FROM AUTHOR]