从LiDAR地图到视觉定位：用于鲁棒点-线-面位姿估计的统一视觉关联

中文摘要 AI 辅助

在预先构建的LiDAR地图中进行相机定位，为长期机器人导航提供了持久的几何参考，但由于相机图像与点云地图之间存在显著的模态差异，这一任务仍然具有挑战性。我们提出了一种统一的定位框架，该框架使LiDAR地图具有视觉可寻址性，而非依赖于专门的图像-LiDAR对应模型。地图几何和反射率被渲染为具有明确2D-3D来源的LiDAR派生准图像，使相机观测和渲染地图视图能够共享成熟的视觉特征和匹配器，用于全局定位和连续位姿跟踪。通过这一通用视觉接口建立点和线对应关系，而保留的来源信息恢复度量LiDAR几何和线支持的平面约束，用于位姿估计。为了提高在模糊关联和弱几何条件下的鲁棒性，我们进一步引入了一种分布感知、可观测性互补的优化策略。该方法不将匹配模糊性简化为标量置信度，而是将候选关联分布传播为方向性位姿信息不确定性，并根据结构因子补充当前弱位姿方向的能力，选择性地强化可靠的结构因子。在EuRoC MAV基准和自采真实世界序列上的实验表明，仅使用预构建的LiDAR地图作为持久先验，即可实现准确的全局定位和鲁棒的连续6自由度跟踪，包括在严重光照变化和动态遮挡条件下。

英文摘要

Camera localization in a prior LiDAR map provides a persistent geometric reference for long-term robotic navigation, yet remains challenging because of the substantial modality gap between camera images and point-cloud maps. We present a unified localization framework that makes the LiDAR map visually addressable rather than relying on a dedicated image-LiDAR correspondence model. Map geometry and reflectivity are rendered into LiDAR-derived quasi-images with explicit 2D-3D provenance, enabling camera observations and rendered map views to share mature visual features and matchers for both global localization and continuous pose tracking. Point and line correspondences are established through this common visual interface, while the retained provenance recovers metric LiDAR geometry and line-supported planar constraints for pose estimation. To improve robustness under ambiguous associations and weak geometry, we further introduce a distribution-aware, observability-complementary optimization strategy. Instead of reducing matching ambiguity to a scalar confidence, candidate association distributions are propagated into directional pose-information uncertainty, and reliable structural factors are selectively reinforced according to their ability to complement the currently weak pose directions. Experiments on the EuRoC MAV benchmark and self-collected real-world sequences demonstrate accurate global localization and robust continuous 6-DoF tracking using only a pre-built LiDAR map as the persistent prior, including under severe illumination variations and dynamic occlusions.

发表机构

• Shanghai Jiao Tong University（上海交通大学）
• Beijing Jiaotong University（北京交通大学）

机构由 AI 辅助整理，请以论文原文为准。