Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICRA40945.2020.9197567 | ICRA |
DocType | Volume | Issue |
Conference | 2020 | 1 |
Citations | PageRank | References |
1 | 0.34 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Qin | 1 | 2 | 0.69 |
| Haoyang Ye | 2 | 17 | 6.84 |
| Christian E. Pranata | 3 | 1 | 0.34 |
| Jun Han | 4 | 1 | 0.34 |
| Shuyang Zhang | 5 | 4 | 2.40 |
| Ming Liu | 6 | 775 | 94.83 |