PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization

Abstract

Reliable long-term localization is key for robotic systems in dynamic environments. In this paper, we propose a novel approach for long-term localization using 3D LiDARs, coined PoseMap. In essence, we extract distinctive features from range measurements and bundle these into local views along with observation poses. The sensor's trajectory is then estimated in a sliding window fashion by matching current and old features and minimizing the distances in-between. The map representation facilitates finding a suitable set of old features, by selecting the closest local map(s) for matching. Similarly to a visibility analysis, this procedure provides a suitable set of features for localization but at a fraction of the computational cost. PoseMap also allows for updates and extensions of the map at any time by replacing and adding local maps when necessary. We evaluate our approach using two platforms both equipped with a 3D LiDAR and an IMU, demonstrating localization at 8 Hz and robustness to changes in the environment such as moving vehicles and changing vegetation. PoseMap was implemented on an autonomous vehicle allowing it to drive autonomously over a period of 18 months through a mix of industrial and unstructured off-road environments, covering more than 100 kms without a single localization failure.