Accurate and Robust Object SLAM With 3D Quadric Landmark Reconstruction in Outdoors
Rui Tian, Yunzhou Zhang, Yonghui Feng, Linghao Yang, Zhenzhong Cao, Sonya Coleman, Dermot Kerr
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.