Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping
Published in IEEE/CVF International Conference on Computer Vision (ICCV), 2025
Emanuele Giacomini, Luca Di Giammarino, Lorenzo De Rebotti, Giorgio Grisetti, Martin R. Oswald
Abstract
LiDARs provide accurate geometric measurements, making them valuable for ego-motion estimation and reconstruction tasks. However, managing an accurate and lightweight environment representation remains challenging. This work pioneers the use of Gaussian primitives for a LiDAR odometry and mapping pipeline, using spherical projection to refine scene representation from LiDAR data. The approach achieves state-of-the-art mapping performance with minimal GPU requirements, while matching current standards in registration accuracy. Efficiency and effectiveness make it a promising step toward real-time SLAM solutions.
Resources
Bibtex
```bibtex @inproceedings{giacomini2025splatloam, title={Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping}, author={Giacomini, Emanuele and Di Giammarino, Luca and De Rebotti, Lorenzo and Grisetti, Giorgio and Oswald, Martin R.}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2025} }