Semantic Mapping Based on ORB-SLAM and YOLO in Indoor Scenes

Abstract

Simultaneous Localization and Mapping (SLAM) technology is important in robotics research. With the improvement of hardware accuracy and reliability of depth cameras, the application of depth cameras as the main sensor for indoor navigation robots has become popular. Camera sensors have become the primary means of obtaining environmental information due to low costs and high scene recognition capability. We generated a map for voice navigation by integrating the semantic information of objects detected by the object detection algorithm YOLO into the navigation map created by Visual SLAM. A mapping system was proposed based on object detection and Visual SLAM.