Cao, Zhenzhong

Coleman, Sonya

Feng, Yonghui

Kerr, Dermot

Tian, Rui

Yang, Linghao

Zhang, Yunzhou

English

Object-oriented SLAM is a popular technology inautonomous driving and robotics. In this paper, we proposea stereo visual SLAM with a robust quadric landmark representationmethod. The system consists of four components,including deep learning detection, object-oriented data association,dual quadric landmark initialization and object-based poseoptimization. State-of-the-art quadric-based SLAM algorithmsalways face observation related problems and are sensitive toobservation noise, which limits their application in outdoorscenes. To solve this problem, we propose a quadric initializationmethod based on the decoupling of the quadric parametersmethod, which improves the robustness to observation noise.The sufficient object data association algorithm and the objectorientedoptimization with multiple cues enables a highly accurateobject pose estimation that is robust to local observations.Experimental results show that the proposed system is morerobust to observation noise and significantly outperforms currentstate-of-the-art methods in outdoor environments. In addition,the proposed system demonstrates real-time performance

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Accurate and Robust Object SLAM with 3D Quadric Landmark Reconstruction in Outdoor Environment

https://pure.ulster.ac.uk/ws/files/99735151/Accurate_and_Robust_Object_oriented_SLAM_with_3D_Quadric_Landmark_Construction_in_Outdoor_Environment.pdf

Accurate and Robust Object SLAM with 3D Quadric Landmark Reconstruction in Outdoor Environment

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