用2D高斯点阵实现无需标靶的激光雷达-相机精准标定
Robust LiDAR-Camera Calibration with 2D Gaussian Splatting
- 通过激光点云重建无颜色的2D高斯点阵，结合图像优化颜色与姿态
- 在优化过程中同步估计外参，精度优于传统方法
- 融合重投影与三角化损失，提升标定鲁棒性，适合无人系统部署
激光雷达-相机系统在机器人领域日益普及，其数据融合的关键是系统标定。现有方法多依赖辅助目标物，操作复杂；而无标靶方法尚未达到实用效果。本文利用2D高斯点阵（2DGS）从相机图像序列中重建几何信息，提出一种基于几何约束的标定方法：首先用激光点云重建无颜色的2DGS，再通过最小化光度损失更新高斯点阵颜色，同时优化外参。此外，为克服光度损失局限，引入重投影与三角化损失，显著提升标定精度与鲁棒性。
原文摘要 · Abstract (English)
LiDAR-camera systems have become increasingly popular in robotics recently. A critical and initial step in integrating the LiDAR and camera data is the calibration of the LiDAR-camera system. Most existing calibration methods rely on auxiliary target objects, which often involve complex manual operations, whereas targetless methods have yet to achieve practical effectiveness. Recognizing that 2D Gaussian Splatting (2DGS) can reconstruct geometric information from camera image sequences, we propose a calibration method that estimates LiDAR-camera extrinsic parameters using geometric constraints. The proposed method begins by reconstructing colorless 2DGS using LiDAR point clouds. Subsequently, we update the colors of the Gaussian splats by minimizing the photometric loss. The extrinsic parameters are optimized during this process. Additionally, we address the limitations of the photometric loss by incorporating the reprojection and triangulation losses, thereby enhancing the calibration robustness and accuracy.
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