Problem
Research questions and friction points this paper is trying to address.
LiDAR-inertial odometry
gravity estimation
vertical position error
3D position error
LiDAR outages
Does Online Gravity Estimation Matter? Revisiting a Silent Design Split in LiDAR-Inertial Odometry
🤖 AI Summary
研究对比了LIO系统中在线重力估计的不同配置方法，发现固定重力在连续LiDAR校正下对误差影响不大，建议保持重力和加速度计偏差在线以提高鲁棒性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
gravity estimation
LIDAR-inertial odometry
trajectory-dependent costs
robustness
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