SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

Object SLAM uses additional semantic information to detect and map objects in the scene, in order to improve the system’s perception and map representation capabilities. Previous methods often use quadrics and cuboids to represent objects, especially in monocular systems. However, their simplistic shapes are insufficient for effectively representing various types of objects, leading to a limitation in the accuracy of object maps and consequently impacting downstream task performance. In this paper, we propose a novel approach for representing objects in monocular SLAM using superquadrics (SQ) with shape parameters. Our method utilizes object appearance and geometry information comprehensively, enabling accurate estimation of object poses and adaptation to various object shapes. Additionally, we propose a lightweight data association strategy to accurately associate semantic observations across multiple views with object landmarks. We implement a monocular semantic SLAM system with real-time performance and conduct comprehensive experiments on public datasets. The results show that our method is able to build accurate object maps and outperforms state-of-the-art methods on object representation.