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This research presents NM-LIO, a novel algorithm for multiple LiDAR-inertial odometry that explicitly models and corrects measurement noise discrepancies between different sensors. By integrating a specific noise model to quantify uncertainties across the system, the method improves localization accuracy in robotic applications where heterogeneous sensor setups are common. The study validates this approach on public datasets, demonstrating enhanced robustness over existing state-of-the-art techniques that often overlook such cross-sensor variations.
NM-LIO: Multiple LiDAR-Inertial Odometry Addressing LiDAR Measurement Noise Discrepancy
arXiv:2610.04551v1 Announce Type: new Abstract: Multiple LiDAR-inertial odometry methods have been widely applied in robotic applications owing to their enhanced accuracy and reliability. However, adopting multiple LiDAR systems can be challenging due to the discrepancies in measurement noise across different LiDARs. Existing methods have typically overlooked the noise discrepancies, which can significantly affect accuracy. In this paper, we propose Noise-aware Multiple LiDAR-Inertial Odometry (NM-LIO) that addresses the noise discrepancies. We integrate a noise model to quantify the measurement noise of each LiDAR. Additionally, we estimate the uncertainty of the residuals based on the measurement noise, allowing the measurement model to capture the noise discrepancies. Our proposed method is evaluated on a public multiple LiDAR dataset and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method can accurately estimate the odometry in various environments by accounting for the noise discrepancies.
Abstract:Multiple LiDAR-inertial odometry methods have been widely applied in robotic applications owing to their enhanced accuracy and reliability. However, adopting multiple LiDAR systems can be challenging due to the discrepancies in measurement noise across different LiDARs. Existing methods have typically overlooked the noise discrepancies, which can significantly affect accuracy. In this paper, we propose Noise-aware Multiple LiDAR-Inertial Odometry (NM-LIO) that addresses the noise discrepancies. We integrate a noise model to quantify the measurement noise of each LiDAR. Additionally, we estimate the uncertainty of the residuals based on the measurement noise, allowing the measurement model to capture the noise discrepancies. Our proposed method is evaluated on a public multiple LiDAR dataset and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method can accurately estimate the odometry in various environments by accounting for the noise discrepancies.
10 pages, 3 figures, 1 table. Published in the International Conference on Robot Intelligence Technology and Applications (RiTA 2024)
Robotics (cs.RO)
arXiv:2610.04551 [cs.RO]
(or arXiv:2610.04551v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.04551 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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