An Approach for Construct Semantic Map with Scene Classification and Object Semantic Segmentation

Abstract

The autonomous navigation system of human beings is more efficient than artificial intelligence technologies, as prior knowledge such as semantic information of the objects and environment is effectively utilized by humans in complicated tasks. Therefore, the ability to build a complete and efficient semantic map system is critical for robot autonomous navigation. In this article, we propose a new framework for building an environment semantic map. Specifically, we construct a 2D semantic map by projecting 3D scene semantic information recognized by convolutional neural network onto a 2D plane. 3D reconstruction of the environment is achieved by RGB-D SLAM 3D space mapping algorithm. We simplify the 3D clustering connectability, using only three cues, to achieve real-time boundary-aware detection of foreground objects. The network trained on the public benchmark dataset is employed to classify the material of the foreground extraction object. Experimental results have demonstrated that the proposed framework is effective in the contraction of semantic map and can be efficiently utilized in the real-time semantic slam system.