发表机构
School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学宇航学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究高动态驾驶下激光雷达-IMU融合问题，提出基于加加速度白噪声运动先验的扩展卡尔曼滤波器框架WNOJ-LIO，通过解耦先验预测状态、视IMU为高频测量源，经仿真和实际实验验证，提升了相关性能。
AI 中文摘要
激光雷达惯性里程计（LIO）是自主导航的关键组成部分，但高动态驾驶带来了两个相互关联的挑战：扫描内运动畸变和受振动污染的惯性测量。大多数实时激光雷达惯性处理流程通过积分原始惯性测量单元（IMU）测量来传播系统状态，然后使用传播的轨迹进行点云去畸变，从而将惯性噪声传播到校正后的扫描和后续的扫描到地图配准中。本文提出了WNOJ-LIO，一种基于加加速度白噪声（WNOJ）扩展卡尔曼滤波器（EKF）的激光雷达-IMU融合框架。WNOJ-LIO在\(\R^3 \times \SO(3)\)上采用解耦的WNOJ先验进行状态预测，并将IMU视为高频测量源而非状态传播的驱动因素。然后，所得的后验状态历史用于激光雷达扫描去畸变和后续的点到平面激光雷达更新。解耦的过程模型实现了闭式协方差传播，从而弥合了批处理WNOJ高斯过程（GP）轨迹先验与递归滤波之间的差距。仿真结果表明，与FAST-LIO风格的基线相比，在加速度和角速度去噪、扫描去畸变和定位精度方面有改进。在四组最高速度从53到208 km/h的驾驶路段上使用自动驾驶赛车进行了实际实验，涵盖了广泛的车辆振动水平。实验进一步验证了所提出的方法，并对其在高动态驾驶下估计加速度、角速度、车身框架线速度、姿态和位置的性能进行了全面评估。WNOJ-LIO的源代码可在该https URL公开获取。
英文摘要
LiDAR-inertial odometry (LIO) is a key component of autonomous navigation, but high-dynamic driving exposes two coupled challenges: intra-scan motion distortion and vibration-contaminated inertial measurements. Most real-time LiDAR-inertial pipelines propagate the system state by integrating raw IMU measurements and then use the propagated trajectory for point cloud de-distortion, thereby propagating inertial noise into both the corrected scan and the subsequent scan-to-map registration. This paper presents WNOJ-LIO, a LiDAR-IMU fusion framework based on a White-Noise-on-Jerk (WNOJ) Extended Kalman Filter (EKF). WNOJ-LIO employs a decoupled WNOJ prior on $\R^3 \times \SO(3)$ for state prediction and treats the IMU as a high-frequency measurement source rather than the driver of state propagation. The resulting posterior state history is then used for LiDAR scan de-distortion and subsequent point-to-plane LiDAR updates. The decoupled process model enables closed-form covariance propagation, thereby bridging the gap between batch WNOJ Gaussian process (GP) trajectory priors and recursive filtering. Simulation results demonstrate improvements in acceleration and angular-velocity denoising, scan de-distortion, and localization accuracy over a FAST-LIO-style baseline. Real-world experiments were conducted using an autonomous racing car on four driving segments with maximum speeds ranging from 53 to 208~km/h, covering a wide range of vehicle vibration levels. The experiments further validate the proposed method and provide a comprehensive evaluation of its performance in estimating acceleration, angular velocity, body-frame linear velocity, attitude, and position under highly dynamic driving. The source code of WNOJ-LIO is publicly available at https://github.com/LvJohny/wnoj-ekf-lio.git.
