发表机构
KAIST (Korea Advanced Institute of Science and Technology); Hanwha Aerospace（韩国科学技术院; 韩华航空航天）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出NM-LIO，通过噪声模型量化各LiDAR测量噪声并估计残差不确定性，解决多LiDAR系统噪声差异问题，提升里程计估计准确性。
AI 中文摘要
多LiDAR-惯性里程计方法因其增强的准确性和可靠性而广泛应用于机器人应用。然而，由于不同LiDAR之间测量噪声的差异，采用多LiDAR系统可能具有挑战性。现有方法通常忽略了噪声差异，这可能会显著影响准确性。在本文中，我们提出了噪声感知的多LiDAR-惯性里程计（NM-LIO），以解决噪声差异问题。我们整合了一个噪声模型来量化每个LiDAR的测量噪声。此外，我们基于测量噪声估计残差的不确定性，使测量模型能够捕捉噪声差异。我们提出的方法在公开的多LiDAR数据集上进行了评估，并与最先进的方法进行了比较。实验结果表明，所提出的方法通过考虑噪声差异，能够在各种环境中准确估计里程计。
英文摘要
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.
Comments10 pages, 3 figures, 1 table. Published in the International Conference on Robot Intelligence Technology and Applications (RiTA 2024)
