  We present LINS, a lightweight lidar-inertial state estimator, for real-time
ego-motion estimation. The proposed method enables robust and efficient
navigation for ground vehicles in challenging environments, such as
feature-less scenes, via fusing a 6-axis IMU and a 3D lidar in a
tightly-coupled scheme. An iterated error-state Kalman filter (ESKF) is
designed to correct the estimated state recursively by generating new feature
correspondences in each iteration, and to keep the system computationally
tractable. Moreover, we use a robocentric formulation that represents the state
in a moving local frame in order to prevent filter divergence in a long run. To
validate robustness and generalizability, extensive experiments are performed
in various scenarios. Experimental results indicate that LINS offers comparable
performance with the state-of-the-art lidar-inertial odometry in terms of
stability and accuracy and has order-of-magnitude improvement in speed.
