FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
This addresses the need for reliable, georeferenced point cloud maps in autonomous driving applications, though it appears incremental as it builds on existing SLAM methods.
The authors tackled the problem of internal distortion and missing georeferencing in point cloud maps from SLAM, which prevents their use in map-based localization for autonomous driving, by proposing FlexCloud, an automatic method that directly georeferences and corrects drift using GNSS data and a 3D rubber-sheet transformation, enabling consistent, globally referenced maps.
Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An important field of research is the generation of point cloud maps, referring to the topic of simultaneous localization and mapping (SLAM). As most recent developments do not include global position data, the resulting point cloud maps suffer from internal distortion and missing georeferencing, preventing their use for map-based localization approaches. Therefore, we propose FlexCloud for an automatic georeferencing of point cloud maps created from SLAM. Our approach is designed to work modularly with different SLAM methods, utilizing only the generated local point cloud map and its odometry. Using the corresponding GNSS positions enables direct georeferencing without additional control points. By leveraging a 3D rubber-sheet transformation, we can correct distortions within the map caused by long-term drift while maintaining its structure. Our approach enables the creation of consistent, globally referenced point cloud maps from data collected by a mobile mapping system (MMS). The source code of our work is available at https://github.com/TUMFTM/FlexCloud.