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.

FlexMap Fusion: Georeferencing and Automated Conflation of HD Maps with OpenStreetMap

Today's software stacks for autonomous vehicles rely on HD maps to enable sufficient localization, accurate path planning, and reliable motion prediction. Recent developments have resulted in pipelines for the automated generation of HD maps to reduce manual efforts for creating and updating these HD maps. We present FlexMap Fusion, a methodology to automatically update and enhance existing HD vector maps using OpenStreetMap. Our approach is designed to enable the use of HD maps created from LiDAR and camera data within Autoware. The pipeline provides different functionalities: It provides the possibility to georeference both the point cloud map and the vector map using an RTK-corrected GNSS signal. Moreover, missing semantic attributes can be conflated from OpenStreetMap into the vector map. Differences between the HD map and OpenStreetMap are visualized for manual refinement by the user. In general, our findings indicate that our approach leads to reduced human labor during HD map generation, increases the scalability of the mapping pipeline, and improves the completeness and usability of the maps. The methodological choices may have resulted in limitations that arise especially at complex street structures, e.g., traffic islands. Therefore, more research is necessary to create efficient preprocessing algorithms and advancements in the dynamic adjustment of matching parameters. In order to build upon our work, our source code is available at https://github.com/TUMFTM/FlexMap_Fusion.

Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving

Autonomous vehicles require accurate and robust localization and mapping algorithms to navigate safely and reliably in urban environments. We present a novel sensor fusion-based pipeline for offline mapping and online localization based on LiDAR sensors. The proposed approach leverages four LiDAR sensors. Mapping and localization algorithms are based on the KISS-ICP, enabling real-time performance and high accuracy. We introduce an approach to generate semantic maps for driving tasks such as path planning. The presented pipeline is integrated into the ROS 2 based Autoware software stack, providing a robust and flexible environment for autonomous driving applications. We show that our pipeline outperforms state-of-the-art approaches for a given research vehicle and real-world autonomous driving application.