Long-Term Map-Maintenance in Changing Environments using Ray-Bundle-Impact-Factor Estimation

Ensuring an accurate and robust localization is one of the most significant problems in the field of mobile robotics. In this context, map-based localization methods utilizing 3D LiDARS for environmental perception are widely used. Even if there exist multiple promising techniques in this field, the majority of approaches can only guarantee an accurate and robust operation if there is no deviation between the map and the real surroundings. Consequently, state of the art localization methods frequently suffer from unreliable results or even complete failure in the case of changing environments. In this paper, we propose an efficient technique for a precise and robust maintenance of localization maps through real-time incorporation of 3D LiDAR scans. Our map update procedure is based on a novel way of estimating the interference between laserbeams and map contents, denoted as Ray-Bundle-Impact-Factor (RBIF). Our technique additionally solves the widespread problem of disruptive hole creation caused by discretization effects. Experiments on real-world as well as synthetic data demonstrate the precision and stability of our method under various challenging conditions and evaluate our approach in comparison to multiple SOTA map maintenance algorithms.