论文标题
SQ-SLAM：基于超四个对象表示的单眼语义大满贯
SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation
论文作者
论文摘要
Object SLAM使用其他语义信息来检测和映射场景中的对象，以提高系统的感知和映射表示功能。四边形和立方体通常用于表示对象，但是它们的单个形状限制了对象图的准确性，从而影响下游任务的应用。在本文中，我们将具有形状参数的超季度（SQ）引入猛击中以表示对象，并提出了一种单独的参数估计方法，该方法可以准确估计对象姿势并适应不同的形状。此外，我们提出了一种轻巧的数据关联策略，用于将多个视图中的语义观察与对象地标正确关联。我们通过实时性能实施一个单眼语义大满贯系统，并在公共数据集上进行全面的实验。结果表明，我们的方法能够构建准确的对象映射，并且在对象表示中具有优势。代码将在接受后发布。
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. Quadrics and cubes are often used to represent objects, but their single shape limits the accuracy of object map and thus affects the application of downstream tasks. In this paper, we introduce superquadrics (SQ) with shape parameters into SLAM for representing objects, and propose a separate parameter estimation method that can accurately estimate object pose and adapt to different shapes. Furthermore, we present a lightweight data association strategy for correctly associating semantic observations in 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 map and has advantages in object representation. Code will be released upon acceptance.
