An improved particle filter for sparse environments

in this paper, we combine a path planner based on boundary value problems (bvp) and monte carlo localization (mcl) to solve the wake-up robot problem in a sparse environment. this problem is difficult since large regions of sparse environments do not provide relevant information for the robot to recover its pose. we propose a novel method that distributes particle poses only in relevant parts of the environment and leads the robot along these regions using the numeric solution of a bvp. several experiments show that the improved method leads to a better initial particle distribution and a better convergence of the localization process.