Object-Aware SLAM Based on Efficient Quadric Initialization and Joint Data Association

Zhenzhong Cao, Yunzhou Zhang, Rui Tian, Rong Ma, Xinggang Hu, Sonya Coleman, Dermot Kerr

Research output: Contribution to conferencePaperpeer-review

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Semantic simultaneous localization and mapping (SLAM) is a popular technology enabling indoor mobile robots to sufficiently perceive and interact with the environment. In this paper, we propose an object-aware semantic SLAM system, which consists of a quadric initialization method, an object-level data association method, and a multi-constraint optimization factor graph. To overcome the limitation of multi-view observations and the requirement of dense point clouds for objects, an efficient quadric initialization method based on object detection and surfel construction is proposed, which can efficiently initialize quadrics within fewer frames and with small viewing angles. The robust object-level joint data association method and the tightly coupled multi-constraint factor graph for quadrics optimization and joint bundle adjustment enable the accurate estimation of constructed quadrics and camera poses. Extensive experiments using public datasets show that the proposed system achieves competitive performance with respect to accuracy and robustness of object quadric estimation and camera localization compared with state-of-the-art methods.
Original languageEnglish
Publication statusAccepted/In press - 30 Jun 2022
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - Kyoto, Japan
Duration: 23 Oct 202227 Oct 2022


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2022
Internet address


  • SLAM
  • Quadric
  • Object-aware
  • Deep-learning


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