Pose tracking using laser scanning and minimalistic environmental models

Keeping track of the position and orientation over time using sensor data, i.e., pose tracking, is a central component in many mobile robot systems. In this paper, we present a Kalman filter-based approach utilizing a minimalistic environmental model. By continuously updating the pose, matching the sensor data to the model is straightforward and outliers can be filtered out effectively by validation gates. The minimalistic model paves the way for a low-complexity algorithm with a high degree of robustness and accuracy. Robustness here refers both to being able to track the pose for a long time, but also handling changes and clutter in the environment. This robustness is gained by the minimalistic model only capturing the stable and large scale features of the environment. The effectiveness of the pose tracking is demonstrated through a number of experiments, including a run of 90 min., which clearly establishes the robustness of the method.