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EllipseLIO introduces a novel ellipsoid representation for LiDAR Inertial Odometry, enabling real-time navigation across heterogeneous environments without scenario-specific tuning. By adapting filtering and registration methods dynamically, it achieves a 35% reduction in odometry error compared to existing state-of-the-art approaches while maintaining robustness where others diverge. This advancement directly addresses critical challenges in autonomous robotics by improving sensor generalization and reliability in GPS-denied scenarios.
EllipseLIO: Adaptive LiDAR Inertial Odometry with an Ellipsoid Representation
arXiv:2605.21150v2 Announce Type: replace Abstract: LiDAR Inertial Odometry (LIO) is a critical component for many mobile robots that need to navigate without relying on external positioning (e.g., GPS). Platforms that operate autonomously in different environments and with heterogeneous LiDAR sensors require a LIO approach that can adapt to these different scenarios without human intervention. Existing LIO approaches can typically provide reliable and accurate odometry in scenarios with similar environments and sensors when suitably tuned. However, many approaches struggle to retain robust odometry across heterogeneous environments and sensors while using a consistent configuration. This paper presents EllipseLIO, a real-time LIO approach that generalises between scenarios by using methods for LiDAR scan filtering and registration that adapt to the sensor capabilities and environment without requiring scenario-specific tuning. Experiments with EllipseLIO and state-of-the-art LIO approaches on five datasets with diverse and challenging scenarios demonstrate that EllipseLIO is the best performing approach overall. It achieves a 35% lower odometry error on average than the second-best approach and is the only approach that does not diverge in any experiment. An open-source version of EllipseLIO is available at https://github.com/v4rl-ucy/ellipselio.
Abstract:LiDAR Inertial Odometry (LIO) is a critical component for many mobile robots that need to navigate without relying on external positioning (e.g., GPS). Platforms that operate autonomously in different environments and with heterogeneous LiDAR sensors require a LIO approach that can adapt to these different scenarios without human intervention. Existing LIO approaches can typically provide reliable and accurate odometry in scenarios with similar environments and sensors when suitably tuned. However, many approaches struggle to retain robust odometry across heterogeneous environments and sensors while using a consistent configuration. This paper presents EllipseLIO, a real-time LIO approach that generalises between scenarios by using methods for LiDAR scan filtering and registration that adapt to the sensor capabilities and environment without requiring scenario-specific tuning. Experiments with EllipseLIO and state-of-the-art LIO approaches on five datasets with diverse and challenging scenarios demonstrate that EllipseLIO is the best performing approach overall. It achieves a 35% lower odometry error on average than the second-best approach and is the only approach that does not diverge in any experiment. An open-source version of EllipseLIO is available at this https URL.
8 pages, 6 figures, 4 tables. Video available at this https URL Code available at this https URL
Robotics (cs.RO)
arXiv:2605.21150 [cs.RO]
(or arXiv:2605.21150v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.21150 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/LRA.2026.3726372 Focus to learn more DOI(s) linking to related resources
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