A Shared Model Based Dense Real-Time Semantic SLAM Method Towards Repetitive Scene

摘要

Dense Simultaneous localization and mapping has attracted people's attention in recent years. However, it always consists a large map which led to an increase in storage space and generates incomplete map. In this paper, we designed a semantic SLAM system which reduce map storage space while improving integrity. The key idea is to segment objects from the background to individual models using deep neural network and reconstruct the models of same class with a common map storage space. We built a complete dense semantic system and propose a method to match two same objects in large distance.