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.

翻译：现有的激光雷达-惯性里程计系统在状态估计中通常采用传感器特定或环境依赖的测量协方差，这导致参数调优繁琐，且在挑战性条件下（如传感器退化与观测噪声）性能欠佳。为此，本文提出一种自适应卡尔曼滤波框架，能够动态估计激光雷达与惯性测量单元测量时变噪声协方差，实现传感器间基于场景感知的置信度加权。在激光雷达退化时，系统优先采用IMU数据，同时抑制来自移动物体或含噪点云等不可靠输入的贡献。此外，引入一种紧凑的基于高斯分布的地图表征方法，以建模环境平面性与空间噪声。通过伪融合相关配准策略，即使在森林等非结构化环境中也能实现精确的平面法向估计。大量实验验证了所提系统在多样化环境中的鲁棒性，包括动态场景与几何退化场景。本方法在所有MARS-LVIG序列上均获得可靠的定位结果，并在KITTI里程计基准测试中位列第8名。代码发布于https://github.com/xpxie/AKF-LIO.git。