论文标题
Fast-Lio：通过紧密耦合的迭代Kalman Filter，快速，健壮
FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
论文作者
论文摘要
本文提出了一个计算高效且健壮的激光惯性探测框架。我们使用紧密耦合的迭代扩展的Kalman滤波器将LIDAR特征点与IMU数据融合在一起，以允许在发生变性的快速运动，嘈杂或混乱的环境中进行稳健的导航。为了降低大量测量值的计算负载，我们提出了一个新公式来计算卡尔曼增益。新公式的计算负载取决于状态维度而不是测量维度。提出的方法及其实现在各种室内和室外环境中进行了测试。在所有测试中，我们的方法都会实时产生可靠的导航结果：在四极管上的计算机上运行，它在扫描中融合了1,200多个有效的功能点，并在25毫秒内完成了IEKF步骤的所有迭代。我们的代码是在GitHub上开源的。
This paper presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various indoor and outdoor environments. In all tests, our method produces reliable navigation results in real-time: running on a quadrotor onboard computer, it fuses more than 1,200 effective feature points in a scan and completes all iterations of an iEKF step within 25 ms. Our codes are open-sourced on Github.
