Dynamic-LIVO：一种利用时空法线的动态感知激光雷达-惯性-视觉里程计系统
Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals
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- University of Manchester（曼彻斯特大学）
- University of Edinburgh（爱丁堡大学）
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中文总结 AI 辅助
本文提出Dynamic-LIVO，一种利用时空法线分析识别动态点并采用时间延迟策略提升分类可靠性的激光雷达-惯性-视觉里程计系统，在动态环境中实现高精度定位与静态彩色建图。
中文摘要 AI 辅助
本文提出了Dynamic-LIVO，一种动态感知的激光雷达-惯性-视觉里程计（LIVO）系统，用于在动态环境中进行鲁棒的状态估计和静态彩色地图构建。Dynamic-LIVO采用时空（S-T）法线分析来识别动态激光雷达点，并将所得分类结果传播到激光雷达-惯性更新和视觉-惯性更新中，从而防止动态激光雷达测量及其相关的视觉观测影响状态估计和地图构建。然而，在新观测到的且空间稀疏的区域中，由于时空观测不足，S-T法线估计可能不可靠。为解决这一问题，我们引入了一种时间延迟的S-T法线估计策略，该策略推迟对约束不足的点的分类，并在获得额外观测时重新评估这些点。该策略提高了动态分类的可靠性，同时保留了有效的静态点用于地图构建。在具有不同传感器配置的公开数据集和自采数据集上进行的大量实验表明，Dynamic-LIVO在具有挑战性的动态环境中提高了定位精度，并生成了更干净的静态彩色地图。源代码和自采数据集将在论文被接收后公开发布。
英文摘要
This paper proposes Dynamic-LIVO, a dynamic-aware LiDAR-Inertial-Visual Odometry (LIVO) system for robust state estimation and static colored mapping in dynamic environments. Dynamic-LIVO employs Spatio-Temporal (S-T) normal analysis to identify dynamic LiDAR points and propagates the resulting classification to both LiDAR-inertial and visual-inertial updates, preventing dynamic LiDAR measurements and their associated visual observations from affecting state estimation and mapping. However, S-T normal estimation can be unreliable in newly observed and spatially sparse regions due to insufficient spatio-temporal observations. To address this issue, we introduce a time-delayed S-T normal estimation strategy that defers the classification of insufficiently constrained points and re-evaluates them as additional observations become available. This strategy improves dynamic classification reliability while preserving valid static points for map construction. Extensive experiments on public and self-collected datasets with diverse sensor configurations demonstrate that Dynamic-LIVO improves localization accuracy and produces cleaner static colored maps in challenging dynamic environments. The source code and self-collected dataset will be publicly released upon acceptance.
