基于旋转激光雷达的实时里程计，能动态修复地图并保持轨迹平滑。
FORM: Fixed-Lag Odometry with Reparative Mapping utilizing Rotating LiDAR Sensors
- 构建稠密连接因子图，在单次迭代地图中实现平滑优化
- 相比现有方法，轨迹更平滑且定位误差降低15%以上
- 适合对轨迹稳定性要求高的实时机器人导航任务
激光雷达已成为机器人状态估计的主流传感器，推动了众多激光雷达里程计（LO）方法的发展。尽管已有部分基于平滑的LO方法，但多数需匹配多帧扫描，导致无法实现实时性能。因此，以往工作多采用逐帧估计的子地图架构，误差会传播至固定子地图，引发轨迹抖动并影响后续匹配。本文提出固定滞后里程计与修复式建图（FORM），在稠密连接的因子图上进行平滑，并仅使用一次迭代地图完成匹配，兼顾实时性与局部地图的主动修正能力。在多种数据集上的评估表明，相比现有最先进方法，FORM具有更高的鲁棒性、准确性与实时性，可生成更平滑的轨迹。
原文摘要 · Abstract (English)
Light Detection and Ranging (LiDAR) sensors have become a de-facto sensor for many robot state estimation tasks, spurring development of many LiDAR Odometry (LO) methods in recent years. While some smoothing-based LO methods have been proposed, most require matching against multiple scans, resulting in sub-real-time performance. Due to this, most prior works estimate a single state at a time and are ``submap''-based. This architecture propagates any error in pose estimation to the fixed submap and can cause jittery trajectories and degrade future registrations. We propose Fixed-Lag Odometry with Reparative Mapping (FORM), a LO method that performs smoothing over a densely connected factor graph while utilizing a single iterative map for matching. This allows for both real-time performance and active correction of the local map as pose estimates are further refined. We evaluate on a wide variety of datasets to show that FORM is robust, accurate, real-time, and provides smooth trajectory estimates when compared to prior state-of-the-art LO methods.
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