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Object-oriented SLAM is a popular technology in autonomous driving androbotics. In this paper, we propose a stereo visual SLAM with a robust quadriclandmark representation method. The system consists of four components,including deep learning detection, object-oriented data association, dualquadric landmark initialization and object-based pose optimization.State-of-the-art quadric-based SLAM algorithms always face observation relatedproblems and are sensitive to observation noise, which limits their applicationin outdoor scenes. To solve this problem, we propose a quadric initializationmethod based on the decoupling of the quadric parameters method, which improvesthe robustness to observation noise. The sufficient object data associationalgorithm and object-oriented optimization with multiple cues enables a highlyaccurate object pose estimation that is robust to local observations.Experimental results show that the proposed system is more robust toobservation noise and significantly outperforms current state-of-the-artmethods in outdoor environments. In addition, the proposed system demonstratesreal-time performance.

Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment