使用LiDAR基准标记的建图与定位
Mapping and Localization Using LiDAR Fiducial Markers
AI总结：
本论文针对LiDAR基准标记落后于视觉基准标记的问题，提出基于强度图像和几何信息的LFM建图与定位框架，实现低重叠点云配准与3D地图合并，并引入Livox-3DMatch数据集，实验验证其优越性。
摘要：
LiDAR传感器对自主系统至关重要，然而LiDAR基准标记（LFMs）在采用率和实用性方面落后于视觉基准标记（VFMs）。弥合这一差距对机器人学和计算机视觉至关重要，但由于3D LiDAR数据的稀疏、非结构化特性以及以2D为中心的基准标记设计，这一任务具有挑战性。本论文提出了一种使用LFMs进行建图与定位的新框架，以惠及多种实际应用，包括采集3D资产和用于点云配准的训练数据、3D地图合并、增强现实（AR）等。首先，引入了一种基于强度图像的LiDAR基准标记（IFM）系统，使用与VFMs兼容的薄型信纸尺寸标记。一种检测方法从强度图像中定位3D基准标记，从而实现LiDAR位姿估计。其次，一种增强算法将检测扩展到3D地图，增加了标记检测范围，并促进了3D地图合并等任务。该方法同时利用强度和几何信息，克服了仅依赖几何的检测方法的局限性。第三，提出了一种新的基于LFM的建图与定位方法，用于配准无序、低重叠的点云。该方法采用自适应阈值检测和两级图框架来求解最大后验（MAP）问题，优化点云和标记位姿。此外，还引入了Livox-3DMatch数据集，改进了基于学习的多视图点云配准方法。在多种室内外场景中使用不同LiDAR模型进行的大量实验证明了所提出框架的有效性和优越性。
英文摘要：
LiDAR sensors are essential for autonomous systems, yet LiDAR fiducial markers (LFMs) lag behind visual fiducial markers (VFMs) in adoption and utility. Bridging this gap is vital for robotics and computer vision but challenging due to the sparse, unstructured nature of 3D LiDAR data and 2D-focused fiducial marker designs. This dissertation proposes a novel framework for mapping and localization using LFMs is proposed to benefit a variety of real-world applications, including the collection of 3D assets and training data for point cloud registration, 3D map merging, Augmented Reality (AR), and many more. First, an Intensity Image-based LiDAR Fiducial Marker (IFM) system is introduced, using thin, letter-sized markers compatible with VFMs. A detection method locates 3D fiducials from intensity images, enabling LiDAR pose estimation. Second, an enhanced algorithm extends detection to 3D maps, increasing marker range and facilitating tasks like 3D map merging. This method leverages both intensity and geometry, overcoming limitations of geometry-only detection approaches. Third, a new LFM-based mapping and localization method registers unordered, low-overlap point clouds. It employs adaptive threshold detection and a two-level graph framework to solve a maximum a-posteriori (MAP) problem, optimizing point cloud and marker poses. Additionally, the Livox-3DMatch dataset is introduced, improving learning-based multiview point cloud registration methods. Extensive experiments with various LiDAR models in diverse indoor and outdoor scenes demonstrate the effectiveness and superiority of the proposed framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
