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

• URL: http://arxiv.org/abs/2110.08977v1
• Date: Mon, 18 Oct 2021 02:03:51 GMT
• Title: Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment
• Authors: Rui Tian, Yunzhou Zhang, Yonghui Feng, Linghao Yang, Zhenzhong Cao, Sonya Coleman, Dermot Kerr
• Abstract summary: We propose a stereo visual SLAM with a robust quadric landmark representation method. The proposed system is more robust to observation noise and significantly outperforms current state-of-the-art methods in outdoor environments.
• Score: 4.881705044039887
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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