3D LiDAR Map-Based Robust Localization System Leveraging Pose Divergence Detection and Relocalization

In recent years, various studies on simultaneous localization and mapping (SLAM) have achieved outstanding performance in terms of accuracy. Accordingly, various SLAM methods can generate a precise 3D map of the surroundings in usual environments and estimate the pose accurately on a pre-built map. However, the localization should be not only accurate but also robust in various situations to achieve a fully autonomous navigation system. Unfortunately, existing localization algorithms are not robust in some cases. For example, the aggressive walking motion of quadruped robots frequently causes a divergence of the odometry algorithm, leading to a catastrophic failure of a fully autonomous system. In this study, we propose a robust localization system leveraging a pose divergence manager, which is applicable to various odometry algorithms. The localization system integrates a pose divergence manager with a 3D LiDAR map-based global localizer that estimates the global pose of the robot on the pre-built 3D LiDAR map. We conducted real-world experiments using a quadruped robot and verified that our proposed method is accurate and robust in indoor and outdoor environments.