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.
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 61973066 and 61471110, in part by the Major Science and Technology Projects of Liaoning Province under Grant 2021JH1/10400049, in part by the Fundation of Key Laboratory of Aerospace System Simulation under Grant 6142002200301, in part by the Fundation of Key Laboratory of Equipment Reliability under Grant WD2C20205500306, and in part by the Major Science and Technology Innovation Engineering Projects of Shandong Province under Grant 2019JZZY010128.
© 2016 IEEE.
- Simultaneous localization and mapping
- Three-dimensional displays
- Vision-Based Navigation
- Vision-based navigation