Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and in which landmarks are solely used to compute relative constraints between robot poses. In previous work, we have developed efficient methods to build Pose SLAM maps that ponder the information content on odometry and measurement links to keep the graph of poses sparse. In this paper we show results of Pose SLAM mapping with our custom built 3D laser and an outdoor all-terrain mobile robot. Finally, we argue that Pose SLAM graphs can be directly used as belief roadmaps and, thus, used for path planning under uncertainty. We show how to plan trajectories with the lowest accumulated robot pose uncertainty, i.e., the most reliable path to the goal, taking into account the encoded uncertainty in the map. Results of this navigation strategy are demonstrated with our outdoor robot.