Bibliographic Details
| Title: |
LiDAR-enhanced 3D Gaussian Splatting Mapping |
| Authors: |
Shen, Jian, Yu, Huai, Wu, Ji, Yang, Wen, Xia, Gui-Song |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Robotics |
| More Details: |
This paper introduces LiGSM, a novel LiDAR-enhanced 3D Gaussian Splatting (3DGS) mapping framework that improves the accuracy and robustness of 3D scene mapping by integrating LiDAR data. LiGSM constructs joint loss from images and LiDAR point clouds to estimate the poses and optimize their extrinsic parameters, enabling dynamic adaptation to variations in sensor alignment. Furthermore, it leverages LiDAR point clouds to initialize 3DGS, providing a denser and more reliable starting points compared to sparse SfM points. In scene rendering, the framework augments standard image-based supervision with depth maps generated from LiDAR projections, ensuring an accurate scene representation in both geometry and photometry. Experiments on public and self-collected datasets demonstrate that LiGSM outperforms comparative methods in pose tracking and scene rendering.
Comment: Accepted by ICRA 2025 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2503.05425 |
| Accession Number: |
edsarx.2503.05425 |
| Database: |
arXiv |
