用多子图匹配与回溯优化，提升激光里程计的精度和稳定性。
Online Lidar-Only Odometry with Retrospective Refinement of Overlapping Submaps
- 当前扫描同时对齐多个重叠子图，而非单一静态地图。
- 在汽车数据集上显著优于现有方法，长短期精度均提升。
- 适合需要高精度定位的自动驾驶系统使用。
激光里程计旨在从连续激光扫描中估计移动平台的自身运动。传统扫描-地图方法将每帧扫描注册到一个动态演化的单一地图，导致误差随时间累积。为此，我们提出一种多子图方法：当前扫描不再仅与单一地图匹配，而是同时与多个重叠子图进行注册。通过在位姿图中优化这些约束，该方法不仅能精确估计当前位姿，还能回溯性地优化子图锚点，从而提升短时一致性与长时准确性。我们在多个车载数据集上验证了该方法，在保持实时性能的同时，相比多种先进激光里程计方法实现了显著精度提升。消融实验确认了多注册机制和地图回溯优化是精度提升的核心因素。代码与原始结果已公开于 https://github.com/Fusion-Goettingen/RAL_2026_Kurda_Online。
原文摘要 · Abstract (English)
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.
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