基于扩展卡尔曼滤波器的激光雷达-惯性里程计
LiDAR-Inertial Odometry Based on Extended Kalman Filter
AI总结：
提出基于扩展卡尔曼滤波器的KLIO激光雷达-惯性里程计，利用IMU预积分和扫描匹配计算先验与似然，实现精确轨迹跟踪与建图，精度媲美最先进方法。
摘要：
激光雷达-惯性里程计（LIO）通常采用基于优化的方法实现，其中因子图因其能够无缝集成激光雷达和惯性测量单元（IMU）测量的残差而被广泛使用。相反，最近的一项研究表明，使用松耦合方法也可以实现精确的LIO。受这一进展的启发，我们提出了一种利用递归贝叶斯滤波器、通过扩展卡尔曼滤波器（EKF）求解的LIO方法，本文中称为KLIO。在KLIO中，先验分布和似然分布分别通过IMU预积分以及激光雷达与局部地图点云之间的扫描匹配计算，而位姿、速度和IMU偏差则通过EKF过程更新。通过在新学院数据集上的实验，我们证明了KLIO实现了精确的轨迹跟踪和建图。其精度与紧耦合和松耦合方法中的最先进方法相当。
英文摘要：
LiDAR-Inertial Odometry (LIO) is typically implemented using an optimization-based approach, with the factor graph often being employed due to its capability to seamlessly integrate residuals from both LiDAR and IMU measurements. Conversely, a recent study has demonstrated that accurate LIO can also be achieved using a loosely-coupled method. Inspired by this advancements, we present a LIO method that leverages the recursive Bayes filter, solved via the Extended Kalman Filter (EKF) - herein referred to as KLIO. Within KLIO, prior and likelihood distributions are computed using IMU preintegration and scan matching between LiDAR and local map point clouds, and the pose, velocity, and IMU biases are updated through the EKF process. Through experiments with the Newer College dataset, we demonstrate that KLIO achieves precise trajectory tracking and mapping. Its accuracy is comparable to that of the state-of-the-art methods in both tightly- and loosely-coupled methods.
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