On localization uncertainty in an autonomous inspection

This paper presents a multi-goal path planning framework based on a self-organizing map algorithm and a model of the navigation describing evolution of the localization error. The framework combines finding a sequence of goals' visits with a goal-to-goal path planning considering localization uncertainty. The approach is able to deal with local properties of the environment such as expected visible landmarks usable for the navigation. The local properties affect the performance of the navigation, and therefore, the framework can take the full advantage of the local information together with the global sequence of the goals' visits to find a path improving the autonomous navigation. Experimental results in real outdoor and indoor environments indicate that the framework provides paths that effectively decreases the localization uncertainty; thus, increases the reliability of the autonomous goals' visits.