Semantic Mapping Based on Image Feature Fusion in Indoor Environments

Abstract

It is of the utmost importance for the robot to understand human semantic instructions in human-robot interaction. Combining semantic information with SLAM-based maps leads to a semantic map. Deep neural networks are able to extract useful information from the robot's visual information. In this paper, we integrate the RGB feature information extracted by the classification network and the detection network to improve the robot's scene recognition ability and make the acquired semantic information more accurate. The image segmentation algorithm labels the areas of interest in the metric map. Furthermore, the fusion algorithm is incorporated to obtain the semantic information of each area, and the detection algorithm recognizes the key objects in the area. We have demonstrated an efficient combination of semantic information with the occupancy grid map toward accurate semantic mapping.