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

By Chao Qin, Haoyang Ye, Christian E. Pranata, J. Han, Shuyang Zhang, Ming Liu. IEEE International Conference on Robotics and Automation, 2019

334 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2811a64f7eef55599255a1e24f019196293f17f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Fu Zhang
2. Jiarong Lin
3. Daniela Rus
4. Carlo Ratti
5. Dongjiao He
6. Wei Xu
7. Yixi Cai
8. Brendan Englot

In Computer Vision and Pattern Recognition

1. Fu Zhang
2. Jiarong Lin
3. Daniela Rus
4. Carlo Ratti
5. Dongjiao He
6. Wei Xu
7. Yixi Cai
8. Brendan Englot

In Electrical and Electronic Engineering

1. Jiarong Lin
2. Carlo Ratti
3. Daniela Rus
4. Brendan Englot
5. Tixiao Shan
6. Fu Zhang

Citations in the corpus

• LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (2021, methodology): “Specifically designed for ground vehicles, LINS [14] uses an error-state Kalman filter to correct the state of the robot recursively.”
• LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (2020, methodology): “A robocentric lidar-inertial state estimator, LINS, is presented in [16].”
• FAST-LIO2: Fast Direct LiDAR-Inertial Odometry (2021, methodology): “LINS [31] introduces a tightlycoupled iterated Kalman filter and robocentric formula into 3 the LiDAR pose optimization in the odometry.”