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

Rui Tian, Yunzhou Zhang, Yonghui Feng, Linghao Yang, Zhenzhong Cao, Sonya Coleman, Dermot Kerr

Research output: Contribution to conferencePaperpeer-review

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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 the objectoriented
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.
Original languageEnglish
Publication statusAccepted/In press - 31 Jan 2022
Event2022 IEEE International Conference on Robotics and Automation - Philadelphia, USA., Philadelphia, United States
Duration: 23 May 202227 May 2022


Conference2022 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA2022
Country/TerritoryUnited States
Internet address


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