OpenLiDARMap: Zero-Drift Point Cloud Mapping using Map Priors

Accurate localization is a critical component of mobile autonomous systems, especially in Global Navigation Satellite Systems (GNSS)-denied environments where traditional methods fail. In such scenarios, environmental sensing is essential for reliable operation. However, approaches such as LiDAR odometry and Simultaneous Localization and Mapping (SLAM) suffer from drift over long distances, especially in the absence of loop closures. Map-based localization offers a robust alternative, but the challenge lies in creating and georeferencing maps without GNSS support. To address this issue, we propose a method for creating georeferenced maps without GNSS by using publicly available data, such as building footprints and surface models derived from sparse aerial scans. Our approach integrates these data with onboard LiDAR scans to produce dense, accurate, georeferenced 3D point cloud maps. By combining an Iterative Closest Point (ICP) scan-to-scan and scan-to-map matching strategy, we achieve high local consistency without suffering from long-term drift. Thus, we eliminate the reliance on GNSS for the creation of georeferenced maps. The results demonstrate that LiDAR-only mapping can produce accurate georeferenced point cloud maps when augmented with existing map priors.

Three-Dimensional Vehicle Dynamics State Estimation for High-Speed Race Cars under varying Signal Quality

This work aims to present a three-dimensional vehicle dynamics state estimation under varying signal quality. Few researchers have investigated the impact of three-dimensional road geometries on the state estimation and, thus, neglect road inclination and banking. Especially considering high velocities and accelerations, the literature does not address these effects. Therefore, we compare two- and three-dimensional state estimation schemes to outline the impact of road geometries. We use an Extended Kalman Filter with a point-mass motion model and extend it by an additional formulation of reference angles. Furthermore, virtual velocity measurements significantly improve the estimation of road angles and the vehicle's side slip angle. We highlight the importance of steady estimations for vehicle motion control algorithms and demonstrate the challenges of degraded signal quality and Global Navigation Satellite System dropouts. The proposed adaptive covariance facilitates a smooth estimation and enables stable controller behavior. The developed state estimation has been deployed on a high-speed autonomous race car at various racetracks. Our findings indicate that our approach outperforms state-of-the-art vehicle dynamics state estimators and an industry-grade Inertial Navigation System. Further studies are needed to investigate the performance under varying track conditions and on other vehicle types.