LIR-LIVO融合激光、视觉与惯性信息，光照下仍能精准定位。
LIR-LIVO: A Lightweight,Robust LiDAR/Vision/Inertial Odometry with Illumination-Resilient Deep Features
- 用深度关联激光点云，实现特征均匀分布
- 采用轻量级特征匹配，在复杂光照下误差更低
- 适合低算力设备，尤其在暗光环境表现优异
本文提出LIR-LIVO，一种轻量且鲁棒的激光-惯性-视觉里程计系统，专为挑战性光照和退化环境设计。该方法利用基于深度学习的光照鲁棒特征，结合激光-惯性-视觉里程计（LIVO）框架。通过引入深度关联激光点云实现特征均匀分布，并采用Superpoint与LightGlue的自适应特征匹配策略，LIR-LIVO在保持低计算开销的同时，达到当前最优（SOTA）精度与鲁棒性。在NTU-VIRAL、Hilti'22和R3LIVE-Dataset等基准数据集上进行实验，结果表明，其在标准与挑战性数据集上均优于其他SOTA方法。尤其在Hilti'22数据集中，于弱光环境下仍能实现稳定位姿估计。代码已开源，便于推动机器人领域发展。
原文摘要 · Abstract (English)
In this paper, we propose LIR-LIVO, a lightweight and robust LiDAR-inertial-visual odometry system designed for challenging illumination and degraded environments. The proposed method leverages deep learning-based illumination-resilient features and LiDAR-Inertial-Visual Odometry (LIVO). By incorporating advanced techniques such as uniform depth distribution of features enabled by depth association with LiDAR point clouds and adaptive feature matching utilizing Superpoint and LightGlue, LIR-LIVO achieves state-of-the-art (SOTA) accuracy and robustness with low computational cost. Experiments are conducted on benchmark datasets, including NTU-VIRAL, Hilti'22, and R3LIVE-Dataset. The corresponding results demonstrate that our proposed method outperforms other SOTA methods on both standard and challenging datasets. Particularly, the proposed method demonstrates robust pose estimation under poor ambient lighting conditions in the Hilti'22 dataset. The code of this work is publicly accessible on GitHub to facilitate advancements in the robotics community.
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