用激光雷达提升3D高斯泼溅的建图精度与鲁棒性
LiDAR-enhanced 3D Gaussian Splatting Mapping
- 融合图像与激光雷达点云，联合优化位姿与外参
- 激光雷达初始化使起始点更密集可靠，提升建图质量
- 结合激光雷达投影深度图，改善几何与光影还原
本文提出LiGSM，一种基于激光雷达增强的3D高斯泼溅（3DGS）建图框架，通过融合图像与激光雷达点云，构建联合损失函数以估计位姿并优化外参，实现对传感器相对位置变化的动态适应。该方法利用激光雷达点云初始化3DGS，相比稀疏的SfM点提供更密集且可靠的初始结构。在场景渲染中，引入由激光雷达投影生成的深度图作为图像监督的补充，确保几何与光度信息的准确表达。在公开数据集和自采集数据集上的实验表明，LiGSM在位姿跟踪与场景重建方面均优于现有方法。
原文摘要 · Abstract (English)
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.
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