用连续3D高斯图直接计算深度，提升室内视觉里程计精度
Direct Sparse Odometry with Continuous 3D Gaussian Maps for Indoor Environments
- 用连续3D高斯映射替代离散点云，避免插值误差
- 在两个公开数据集上定位精度优于现有方法
- 适合需要高精度定位的机器人与AR应用
精准定位对机器人和增强现实应用（如自主导航）至关重要。基于视觉的方法结合先验地图，旨在以相机成本实现激光雷达级别的定位精度。然而，现有方法在将离散点云地图与密集图像像素关联时，常依赖不可靠的插值过程，导致深度误差并降低位姿估计精度。本文提出一种单目视觉里程计框架，利用连续3D高斯地图，直接为所有提取的高梯度点分配几何一致的深度值，无需插值。在两个公开数据集上的评估表明，该方法在跟踪精度上优于现有方法。相关源代码已开源，供社区使用。
原文摘要 · Abstract (English)
Accurate localization is essential for robotics and augmented reality applications such as autonomous navigation. Vision-based methods combining prior maps aim to integrate LiDAR-level accuracy with camera cost efficiency for robust pose estimation. Existing approaches, however, often depend on unreliable interpolation procedures when associating discrete point cloud maps with dense image pixels, which inevitably introduces depth errors and degrades pose estimation accuracy. We propose a monocular visual odometry framework utilizing a continuous 3D Gaussian map, which directly assigns geometrically consistent depth values to all extracted high-gradient points without interpolation. Evaluations on two public datasets demonstrate superior tracking accuracy compared to existing methods. We have released the source code of this work for the development of the community.
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