That's My Point：面向大规模户外定位的紧凑式基于对象的LiDAR位姿估计
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation
AI总结：
本文提出一种紧凑的基于对象的LiDAR位姿估计方法，通过语义对象质心与类别压缩点云表示，结合自相关-互相关匹配网络及加权SVD与RANSAC实现配准，在KITTI等数据集上以约一半存储达到SOTA级定位精度。
摘要：
本文研究LiDAR扫描的3D位姿估计问题，旨在以极低的存储需求实现可扩展的建图与定位。我们将分割后扫描的所有点聚类为语义对象，仅用各自的质心和语义类别表示，将每帧LiDAR扫描压缩为四元向量的紧凑集合。这种表示舍弃了场景中对传统配准方法至关重要的结构信息。为缓解该问题，我们提出一种基于自相关与互相关的对象匹配网络，捕捉实体间的几何与语义关系。基于匹配结果，我们通过加权奇异值分解（SVD）和随机抽样一致性（RANSAC）恢复扫描间的相对变换。我们在KITTI数据集上配准不同视角采集的点云，并在KITTI与KITTI-360间进行跨时段定位，验证了该表示足以实现度量级定位。我们的方法达到了与当前最优方法相当的精确度量估计，而表示大小仅约为其一半，平均仅1.33 kB。
英文摘要：
This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all points of segmented scans into semantic objects and representing them only with their respective centroid and semantic class. In this way, each LiDAR scan is reduced to a compact collection of four-number vectors. This abstracts away important structural information from the scenes, which is crucial for traditional registration approaches. To mitigate this, we introduce an object-matching network based on self- and cross-correlation that captures geometric and semantic relationships between entities. The respective matches allow us to recover the relative transformation between scans through weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus (RANSAC). We demonstrate that such representation is sufficient for metric localisation by registering point clouds taken under different viewpoints on the KITTI dataset, and at different periods of time localising between KITTI and KITTI-360. We achieve accurate metric estimates comparable with state-of-the-art methods with almost half the representation size, specifically 1.33 kB on average.
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。
