利用激光雷达强度纹理实现跨模态视觉定位，提升精度与鲁棒性。
Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures
- 基于全景投影构建强度图数据库，融合2D纹理与3D几何信息。
- 通过共视聚类筛选候选图像，提升检索效率与准确性。
- 两阶段2D-3D匹配+共视内点选择，实现高精度6自由度位姿估计。
近年来，跨模态定位受到广泛关注，但先验激光雷达地图中的视觉重定位研究仍较少。现有方法常因2D纹理与3D几何不一致，忽略激光点云中的强度特征。本文提出一种基于强度纹理的先验激光雷达地图中跨模态视觉重定位系统，包含三个模块：地图投影、粗略检索与精细重定位。地图投影模块利用全景投影的密集特性构建强度通道图数据库；粗略检索模块从数据库中检索与查询图像最相似的Top-K结果，并通过共视聚类保留Top-K'个候选；精细重定位模块采用两阶段2D-3D关联与共视内点选择，获得鲁棒对应关系以实现6DoF位姿估计。在自建数据集上的实验表明，该方法在场景识别与位姿估计任务中均表现优异。
原文摘要 · Abstract (English)
Cross-modal localization has drawn increasing attention in recent years, while the visual relocalization in prior LiDAR maps is less studied. Related methods usually suffer from inconsistency between the 2D texture and 3D geometry, neglecting the intensity features in the LiDAR point cloud. In this paper, we propose a cross-modal visual relocalization system in prior LiDAR maps utilizing intensity textures, which consists of three main modules: map projection, coarse retrieval, and fine relocalization. In the map projection module, we construct the database of intensity channel map images leveraging the dense characteristic of panoramic projection. The coarse retrieval module retrieves the top-K most similar map images to the query image from the database, and retains the top-K' results by covisibility clustering. The fine relocalization module applies a two-stage 2D-3D association and a covisibility inlier selection method to obtain robust correspondences for 6DoF pose estimation. The experimental results on our self-collected datasets demonstrate the effectiveness in both place recognition and pose estimation tasks.
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