FAST-LIVGO：一种退化鲁棒的LiDAR-惯性-视觉-GNSS融合里程计
FAST-LIVGO: A Degeneracy-Robust LiDAR-Inertial-Visual-GNSS Fusion Odometry
- College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）
- Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）
- College of Automation, Harbin Engineering University（哈尔滨工程大学自动化学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种基于误差状态迭代卡尔曼滤波的紧耦合LiDAR-惯性-视觉-GNSS融合框架，通过动态时间规整的时空对齐模块、多普勒和时差载波相位观测模型以及退化感知的双模式异常值拒绝策略，在长期大尺度动态环境中实现高精度鲁棒的状态估计。
AI中文摘要：
在长期、大规模和高度动态环境中的鲁棒状态估计与建图仍然是机器人领域的关键挑战。现有的LiDAR-惯性-视觉里程计（LIVO）系统在局部精度上表现良好，但在长距离下会累积漂移，并在几何退化或无纹理场景中可能失效。同时，GNSS辅助融合框架通常依赖LiDAR或视觉里程计进行状态预测和异常值拒绝，使其在里程计退化时变得脆弱。为解决这些局限，我们提出一种基于误差状态迭代卡尔曼滤波的紧耦合LiDAR-惯性-视觉-GNSS融合框架。引入基于动态时间规整的在线时空对齐模块以应对高度动态条件。为更好利用GNSS精度，我们开发了基于多普勒频移和固定锚点时间差载波相位的观测模型，在不增加历史锚点状态的情况下提供毫米级相对约束。我们进一步设计了一种退化感知的双模式异常值拒绝策略，根据LIVO退化程度在LIVO先验引导拒绝和GNSS辅助恢复之间切换。在公开M3DGR数据集和自建20 m/s固定翼无人机数据集上的实验表明，我们的系统减少了累积漂移和地图重影，在精度和鲁棒性上优于现有方法。
英文摘要：
Robust state estimation and mapping in long-term, large-scale, and highly dynamic environments remains a key challenge in robotics. Existing LiDAR-Inertial-Visual Odometry (LIVO) systems achieve strong local accuracy but suffer from accumulated drift over long distances and may fail in geometrically degraded or textureless scenes. Meanwhile, GNSS-aided fusion frameworks often rely on LiDAR or visual odometry for state prediction and outlier rejection, making them vulnerable when odometry degenerates. To address these limitations, we propose a tightly coupled LiDAR-Inertial-Visual-GNSS fusion framework based on an Error-State Iterated Kalman Filter. An online spatiotemporal alignment module using Dynamic Time Warping is introduced for highly dynamic conditions. To better exploit GNSS precision, we develop observation models based on Doppler shifts and fixed-anchor Time-Differenced Carrier Phase, providing millimeter-level relative constraints without augmenting historical anchor states. We further design a degeneracy-aware dual-mode outlier rejection strategy that switches between LIVO-prior-guided rejection and GNSS-aided recovery according to the LIVO degeneracy level. Experiments on the public M3DGR dataset and a custom 20~m/s fixed-wing UAV dataset demonstrate that our system reduces accumulated drift and map ghosting, outperforming state-of-the-art methods in accuracy and robustness.
