An Efficient Object Navigation Strategy for Mobile Robots Based on Semantic Information

In recent years, the rapid development of computer vision makes it possible for mobile robots to be more intelligent. Among the related technologies, the visual SLAM system allows the mobile robot to locate itself, build the map, and provide a navigation strategy to execute follow-up tasks, such as searching for objects in unknown environment according to the observed information. However, most of the existing studies are meant to provide a predefined trajectory for the robot or allow the robot to explore blindly and randomly, which undoubtedly affects the efficiency of the object navigation process and goes against with the idea of "intelligent". To solve the above problems, an efficient object navigation strategy is proposed in this paper. Firstly, a semantic association model is obtained by using the Mask R-CNN and skip-gram to conduct correlation analysis of common indoor objects. Then, with the help of the above model and ROS framework, an effective object navigation strategy is designed to enable the robot to find the given target efficiently. Finally, the classical ORB-SLAM2 system method is integrated to help the robot build a high usability environment map and find passable paths when moving. Simulation results validated that the proposed strategy can efficiently help the robot to navigate to the object without human intervention.