NM-LIO: Multiple LiDAR-Inertial Odometry Addressing LiDAR Measurement Noise Discrepancy

2026-10-03

AI Summary

This study addresses the accuracy degradation in multi-LiDAR odometry caused by heterogeneous sensor measurement noise. To this end, we propose a noise-aware multi-LiDAR-inertial odometry framework. This work is the first to explicitly model inter-sensor measurement noise inconsistencies across multiple LiDARs. By integrating a dedicated noise model, the proposed approach quantifies the noise level of each individual LiDAR and adaptively captures dynamic discrepancies based on residual uncertainty, thereby enabling robust sensor fusion. Extensive experiments conducted on public datasets demonstrate that, compared with existing state-of-the-art methods, the proposed system significantly improves both estimation accuracy and robustness across diverse complex environments.

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.

Problem

• Multiple LiDAR-Inertial Odometry
• Measurement Noise Discrepancy
• Noise-aware
• State Estimation

Innovation

• Multiple LiDAR-Inertial Odometry
• Measurement Noise Discrepancy
• Noise-aware
• Uncertainty Estimation
• Residual Modeling