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

arXiv: https://arxiv.org/abs/2412.02533 · HTML: https://mlsanity.com/paper/2412.02533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Visual-Inertial Mapping with Non-Linear Factor Recovery](https://mlsanity.com/paper/1904.06504/) (2019)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization](https://mlsanity.com/paper/2105.02010/) (2021)
- [CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System](https://mlsanity.com/paper/2109.04687/) (2021)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation](https://mlsanity.com/paper/2210.09739/) (2022)
- [Fast Autonomous Flight in Warehouses for Inventory Applications](https://mlsanity.com/paper/1809.06628/) (2018)
- [Lessons from Robot-Assisted Disaster Response Deployments by the German Rescue Robotics Center Task Force](https://mlsanity.com/paper/2212.09354/) (2022)
- [Autonomous Flight in Unknown GNSS-denied Environments for Disaster Examination](https://mlsanity.com/paper/2103.11742/) (2021)
- [Target Chase, Wall Building, and Fire Fighting: Autonomous UAVs of Team NimbRo at MBZIRC 2020](https://mlsanity.com/paper/2201.03844/) (2022)

## Abstract (arXiv)

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.