发表机构
Anyverse Dynamics（Anyverse Dynamics）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过对比实验发现，在LiDAR-惯性里程计中在线估计重力并非始终有益，建议保持重力和加速度计偏置在线，仅在验证精度提升后使用方向因子。
AI 中文摘要
LiDAR-惯性里程计（LIO）系统在初始化后是否继续估计重力方面存在差异。我们在FAST-LIO2和LIO-SAM中分别比较了四种重力偏置状态配置，并单独测试了一个重力方向因子。在12个数据集序列上使用FAST-LIO2进行评估时，在连续LiDAR校正下固定重力，垂直和3D位置误差的平均配对变化在90%置信区间内位于±2%以内。在LIO-SAM的4个序列上的测试同样表明，在线重力没有一致的好处。与降低范围或视野不同，多秒LiDAR中断揭示了固定重力的轨迹依赖成本。历史匹配的23D到21D切换在LiDAR更新恢复后产生了可重复的3D误差增加。在5秒中断下，使用与预积分相同的IMU的方向因子提高了Hall05上的精度，但在TUHH上使用在线重力时恶化了两个误差。动态启动测试也显示了在特定启动阶段固定偏置的失败。我们建议保持重力和加速度计偏置在线以获得鲁棒性；仅在预期操作条件下验证垂直和3D精度提升后，才使用方向因子。
英文摘要
LiDAR-inertial odometry (LIO) systems differ in whether they continue estimating gravity after initialization. We compare four gravity-bias state configurations in each of FAST-LIO2 and LIO-SAM, then separately test a gravity-direction factor. Across 12 dataset sequences evaluated with FAST-LIO2, fixing gravity under continuous LiDAR correction produces mean paired changes in vertical and 3D position errors with 90% confidence intervals within $\pm 2\%$. Tests on 4 sequences with LIO-SAM likewise show no consistent benefit from online gravity. Multi-second LiDAR outages, unlike reduced range or field of view, reveal trajectory-dependent costs of fixing gravity. A history-matched 23D-to-21D switch places the repeatable 3D error increase after LiDAR updates resume. Under 5-s outages, a direction factor from the same IMU used for preintegration improves accuracy on Hall05 but worsens both errors with online gravity on TUHH. Dynamic-start tests also show fixed-bias failures at particular starting phases. We recommend keeping gravity and accelerometer bias online for robustness; use a direction factor only after verifying vertical and 3D accuracy gains under the intended operating conditions.
Comments8 pages, 5 figures. Code, evidence, and video: https://github.com/jiejie567/rethink-lio-gravity
