Fast minimum uncertainty search on a graph map representation

This paper addresses the problem of path planning considering uncertainty criteria over the belief space. Specifically, we propose a path planning algorithm that uses a novel determinant-based measure of uncertainty and a reduced representation of the environment, in order to obtain the minimum uncertainty path from a roadmap. Our proposal does not require a priori knowledge of the environment due to the construction of the roadmap via a graph-based SLAM algorithm. We report experimental results of our proposal in four datasets that show its feasibility to obtain the minimum uncertainty path towards an autonomous navigation framework and we also show an improvement in the computation time with respect to the state of the art.