Semantic Ground Plane Constraint in Visual SLAM for Indoor Scenes

Abstract

Typical visual simultaneous localization and mapping (SLAM) systems rely on purely geometric elements. Recently semantic SLAM has become popular due to its capability to obtain high-level understanding of the environment. However, most semantic SLAM systems merely aim to produce semantic maps rather than exploiting semantic constraints to boost the accuracy performance. In this paper, we propose a new RGB-D SLAM system for the use in indoor environments. Given that a ground plane represents a significant part of most indoor scenes, our new system exploit a semantic ground plane constraint inferred from a deep neural network along with geometric constraints on the state estimation to improve the performance. More specifically, we introduce a semantically meaningful geometric primitive, namely, a global ground plane to the optimization process of visual SLAM. The global ground model is able to provide an extra constraint on the state estimation through which both frame-to-keyframe and frame-to-model tracking processes are combined, and low-texture ground plane is effectively utilized. Experimental results for the TUM dataset and a real scene have demonstrated the improved performance brought by the semantic constraint in our method.