  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.
