Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment
This addresses robustness issues in SLAM for autonomous driving and robotics, though it is incremental as it builds on existing quadric-based SLAM approaches.
The paper tackles the problem of observation noise sensitivity in object-oriented SLAM for outdoor environments by proposing a robust quadric landmark representation method, resulting in improved robustness and significant outperformance over state-of-the-art methods with real-time performance.
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 object-oriented 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.