NM-LIO: Multiple lidar-inertial odometry addressing lidar measurement noise discrepancy

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 integrates 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.

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Gunhee Shin, Seungjae Lee, Minho Oh, Dongkyu Lee, Jaeyoung Lee, Young-Woo Seo and Hyun Myung, NM-LIO: Multiple lidar-inertial odometry addressing lidar measurement noise discrepancy, In Proceedings of the 12th International Conference on Robot Intelligence Technology and Applications (RiTA-2024), 2024.