LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation

TLDR

We present LINS, a lightweight lidar‑inertial state estimator for real‑time ego‑motion estimation. LINS fuses a 6‑axis IMU and a 3‑D lidar in a tightly‑coupled, robocentric iterated error‑state Kalman filter that generates new feature correspondences each iteration, enabling robust and efficient navigation in challenging, feature‑less environments while keeping computation tractable. Experimental results show LINS matches state‑of‑the‑art lidar‑inertial odometry in stability and accuracy while being an order of magnitude faster.

Abstract

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.