LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Maps

TL;DR

This paper presents a method to improve UAV localization and mapping accuracy in urban environments by registering LiDAR data against georeferenced CityGML models, significantly reducing GNSS errors and producing consistent maps.

Contribution

It introduces a novel LiDAR-based registration approach against CityGML models to refine UAV localization and generate globally consistent maps in challenging environments.

Findings

Reduced GNSS offset errors from 16 m to below 0.5 m

Achieved globally consistent maps aligned with geospatial models

Validated on multiple UAV flights at different sites

Abstract

Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings. These inaccuracies hinder aggregation of diverse data from multiple sources in a unified georeferenced frame for SAR operators. In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses. Besides, LiDAR works best in the vicinity of 3D structures. Hence, we refine coarse GNSS measurements by registering LiDAR maps against CityGML and digital elevation map (DEM) models as a prior for allocentric mapping. An intuitive plausibility score selects the best hypothesis based on occupancy using a 2D height map. Afterwards, we integrate the registration results in a continuous-time spline-based pose graph optimizer with LiDAR odometry and further sensing modalities to obtain globally consistent, georeferenced trajectories and maps. We evaluate the viability of our approach on multiple flights captured at two distinct testing sites. Our method successfully reduced GNSS offset errors from up-to 16 m to below 0.5 m on multiple flights. Furthermore, we obtain globally consistent maps w.r.t. prior 3D geospatial models.