基于图的在线激光雷达里程计与回顾性地图细化
Online Lidar-Only Odometry with Retrospective Refinement of Overlapping Submaps
AI总结：
本文提出基于图的在线激光雷达里程计方法，通过多地图配准和回顾性地图细化提升轨迹估计的精度与长期准确性。
摘要：
激光雷达-only里程计旨在从一系列激光雷达扫描数据流中估计移动平台的轨迹。传统扫描到地图方法将每个扫描数据与单一的演进地图进行配准，这种配准误差会随时间传播。为缓解这一问题，我们提出了一种多地图方法，当前扫描数据被与多个重叠的子地图进行配准，而不是单一静态地图。通过在位姿图中优化所产生的约束，我们的方法不仅能够精确估计当前姿态，还能对子地图的锚点进行回顾性细化，从而提高短期一致性和长期准确性。我们证明，我们的方法在各种汽车数据集上实现了竞争甚至更优的精度，同时保持实时性能。消融研究确认了多个配准和地图回顾性细化作为我们精度提升的核心因素。代码和原始结果可在我们的公共GitHub上获得：https://github.com/Fusion-Goettingen/IROS_2026_Kurda_Graph。
英文摘要：
Lidar odometry aims to estimate the ego-motion of a mobile platform from a stream of lidar scans. Traditional scan-to-map approaches register each scan against a single, evolving map, which propagates registration errors over time. To mitigate this, we propose a multi-submap approach where the current scan is registered against multiple overlapping submaps instead of a single static map. By optimizing the resulting constraints in a pose graph, our method enables not only precise estimation of the current pose, but also retrospective refinement of the submaps' anchor points, which improves short-term consistency and long-term accuracy. We demonstrate that our approach achieves significant accuracy gains over several state-of-the art lidar odometry methods on a variety of automotive datasets while simultaneously maintaining real-time performance. Ablation studies confirm the critical role of multiple registrations and retrospective refinement of the map as core factors for our accuracy gains. Code and raw results are available on our public GitHub at https://github.com/Fusion-Goettingen/RAL_2026_Kurda_Online.
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。
