Spatial semantic graph enhanced monocular SLAM System

Abstract

More and more applications require accurate estimation of objects' poses. For example, Augmented Reality (AR) needs that for interactions among virtual and real objects. However, recent object-level Simultaneous Localization and Mapping (SLAM) systems care more about trajectories than poses of objects. So, in this paper, we present SSG SLAM: a monocular SLAM system based on Spatial Semantic Graph (SSG), leveraging spatial relations to achieve good estimation of objects' poses. First, we put forward the design of SSG to organize objects and their spatial relations. Then, SSG is utilized in a monocular object SLAM and new constraints are proposed based on spatial relations. Finally, experimental evaluations performed on SSG-Dataset show that our approach outperforms the baseline object SLAM system, as well as providing visual consistency in an AR demo.