Abstract
Object-oriented SLAM is a popular technology in
autonomous driving and robotics. In this paper, we propose
a stereo visual SLAM with a robust quadric landmark representation
method. The system consists of four components,
including deep learning detection, object-oriented data association,
dual quadric landmark initialization and object-based 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 decoupling of the quadric parameters
method, which improves the robustness to observation noise.
The sufficient object data association algorithm and the objectoriented
optimization with multiple cues enables 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.
autonomous driving and robotics. In this paper, we propose
a stereo visual SLAM with a robust quadric landmark representation
method. The system consists of four components,
including deep learning detection, object-oriented data association,
dual quadric landmark initialization and object-based 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 decoupling of the quadric parameters
method, which improves the robustness to observation noise.
The sufficient object data association algorithm and the objectoriented
optimization with multiple cues enables 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.
| Original language | English |
|---|---|
| Publication status | Accepted/In press - 31 Jan 2022 |
| Event | 2022 IEEE International Conference on Robotics and Automation - Philadelphia, USA., Philadelphia, United States Duration: 23 May 2022 → 27 May 2022 https://www.icra2022.org/ |
Conference
| Conference | 2022 IEEE International Conference on Robotics and Automation |
|---|---|
| Abbreviated title | ICRA2022 |
| Country/Territory | United States |
| City | Philadelphia |
| Period | 23/05/22 → 27/05/22 |
| Internet address |
