Name
Abstract | ||
|---|---|---|
Object-oriented SLAM is a popular technology in autonomous driving and robotics. In this letter, we propose a stereo visual SLAM with a robust quadric landmark representation method.The system consists of four components, including deep learning detection, quadric landmark initialization, object data association and object pose optimization. State-of-the-art quadric-based SLAM algorithms always face observation-related problems and are sensitive to observation noise, which limits their application in outdoor scenes. To solve this problem, we propose a quadric initialization method based on the separation of the quadric parameters method, which improves the robustness to observation noise. The sufficient object data association algorithm and object-oriented optimization with multiple cues enable a highly accurate object pose estimation that is robust to local observations. Experimental results show that the proposed system is more robust to observation noise and significantly outperforms current state-of-the-art methods in outdoor environments. In addition, the proposed system demonstrates real-time performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LRA.2021.3137896 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Localization, SLAM, vision-based navigation | Journal | 7 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Tian | 1 | 0 | 1.01 |
| Yunzhou Zhang | 2 | 219 | 30.98 |
| Yonghui Feng | 3 | 0 | 0.34 |
| Linghao Yang | 4 | 0 | 0.68 |
| Zhenzhong Cao | 5 | 0 | 0.68 |
| Sonya Coleman | 6 | 16 | 5.59 |
| Dermot Kerr | 7 | 0 | 0.34 |