AKF-LIO：基于自适应卡尔曼滤波的带高斯地图的激光雷达惯性里程计
AKF-LIO: LiDAR-Inertial Odometry with Gaussian Map by Adaptive Kalman Filter
AI总结：
针对现有LIO系统测量协方差依赖传感器与环境、调优繁琐且复杂场景性能差的问题，提出带高斯地图的AKF-LIO框架，通过自适应卡尔曼滤波动态估计时变噪声协方差，结合高斯地图与关联配准策略，在多场景下实现鲁棒精准定位，性能经基准验证。
摘要：
现有激光雷达惯性里程计（LIO）系统在状态估计过程中通常采用传感器专属或依赖环境的测量协方差，导致参数调优繁琐，且在传感器退化、观测含噪等挑战性场景下性能欠佳。为此，我们提出一种自适应卡尔曼滤波（AKF）框架，可动态估计激光雷达与惯性测量单元（IMU）测量值的时变噪声协方差，实现传感器间的上下文感知置信度加权。当激光雷达退化时，系统优先使用IMU数据，同时抑制运动物体、含噪点云等不可靠输入的贡献。此外，我们引入一种紧凑的基于高斯的地图表示方法，用于建模环境的平面性与空间噪声；关联配准策略通过伪合并确保平面法向量估计准确，即便在森林等非结构化环境中也能生效。大量实验验证了所提系统在动态场景、几何退化场景等多样环境下的鲁棒性。该方法在所有MARS-LVIG序列上均取得可靠的定位结果，在KITTI Odometry Benchmark上排名第8。代码将发布于https://github.com/xpxie/AKF-LIO.git。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
