自适应滤波+高斯地图，让激光惯性里程计在复杂环境更稳更准
AKF-LIO: LiDAR-Inertial Odometry with Gaussian Map by Adaptive Kalman Filter
- 用自适应卡尔曼滤波动态估计传感器噪声，实现上下文感知的融合权重
- 在激光数据失效时优先使用惯性数据，有效抑制动态物体和噪点干扰
- 基于高斯图建模环境平面与空间噪声，适合森林等非结构化场景
现有激光惯性里程计（LIO）系统通常依赖特定传感器或环境的测量协方差，导致参数调优繁琐且在挑战性条件下性能不佳（如传感器退化、观测噪声大）。为此，本文提出自适应卡尔曼滤波（AKF）框架，可动态估计激光与惯性测量单元（IMU）测量的时变噪声协方差，实现上下文感知的传感器置信度加权。当激光数据退化时，系统优先采用IMU数据，同时抑制移动物体或噪声点云的贡献。此外，引入紧凑的高斯基地图表示，以建模环境平面性和空间噪声；相关注册策略通过伪合并实现即使在森林等非结构化环境中也能准确估计平面法向量。大量实验验证了该系统在多样化环境中的鲁棒性，涵盖动态场景和几何退化情况。本方法在所有MARS-LVIG序列上均取得可靠定位结果，在KITTI Odometry Benchmark上排名第八。代码将发布于https://github.com/xpxie/AKF-LIO.git。
原文摘要 · Abstract (English)
Existing LiDAR-Inertial Odometry (LIO) systems typically use sensor-specific or environment-dependent measurement covariances during state estimation, leading to laborious parameter tuning and suboptimal performance in challenging conditions (e.g., sensor degeneracy and noisy observations). Therefore, we propose an Adaptive Kalman Filter (AKF) framework that dynamically estimates time-varying noise covariances of LiDAR and Inertial Measurement Unit (IMU) measurements, enabling context-aware confidence weighting between sensors. During LiDAR degeneracy, the system prioritizes IMU data while suppressing contributions from unreliable inputs like moving objects or noisy point clouds. Furthermore, a compact Gaussian-based map representation is introduced to model environmental planarity and spatial noise. A correlated registration strategy ensures accurate plane normal estimation via pseudo-merge, even in unstructured environments like forests. Extensive experiments validate the robustness of the proposed system across diverse environments, including dynamic scenes and geometrically degraded scenarios. Our method achieves reliable localization results across all MARS-LVIG sequences and ranks 8th on the KITTI Odometry Benchmark. The code will be released at https://github.com/xpxie/AKF-LIO.git.
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