  Object SLAM introduces the concept of objects into Simultaneous Localization
and Mapping (SLAM) and helps understand indoor scenes for mobile robots and
object-level interactive applications. The state-of-art object SLAM systems
face challenges such as partial observations, occlusions, unobservable
problems, limiting the mapping accuracy and robustness. This paper proposes a
novel monocular Semantic Object SLAM (SO-SLAM) system that addresses the
introduction of object spatial constraints. We explore three representative
spatial constraints, including scale proportional constraint, symmetrical
texture constraint and plane supporting constraint. Based on these semantic
constraints, we propose two new methods - a more robust object initialization
method and an orientation fine optimization method. We have verified the
performance of the algorithm on the public datasets and an author-recorded
mobile robot dataset and achieved a significant improvement on mapping effects.
We will release the code here: https://github.com/XunshanMan/SoSLAM.
