FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps

TL;DR

FlexCloud offers an automated, modular method for georeferencing and correcting drift in point cloud maps generated from SLAM, enabling globally consistent maps for autonomous driving applications.

Contribution

It introduces a novel approach that combines GNSS data with a 3D rubber-sheet transformation for accurate, global georeferencing and drift correction of SLAM-generated point cloud maps.

Findings

Enables direct georeferencing without control points

Corrects long-term drift in point cloud maps

Produces globally consistent maps from mobile mapping data

Abstract

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.