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This research provides critical empirical evidence regarding state estimation strategies in LiDAR-inertial odometry, specifically demonstrating that fixing gravity bias offers negligible accuracy gains while potentially degrading performance during sensor outages. The findings advocate for maintaining online gravity and accelerometer bias estimation to ensure robustness across diverse operating conditions, directly influencing the design of next-generation autonomous navigation stacks.
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Does Online Gravity Estimation Matter? Revisiting a Silent Design Split in LiDAR-Inertial Odometry

arXiv2 min read

arXiv:2609.13675v1 Announce Type: new 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.

Title:Does Online Gravity Estimation Matter? Revisiting a Silent Design Split in LiDAR-Inertial Odometry

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.

8 pages, 5 figures. Code, evidence, and video: this https URL

Robotics (cs.RO)

arXiv:2609.13675 [cs.RO]

(or arXiv:2609.13675v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.13675 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 285 words

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