A Robot Visual SLAM Algorithm Based on Cloud Computing

Abstract

Visual SLAM is becoming a hot spot mobile robot navigation. When executing SLAM tasks, traditional robots have many shortcomings, Huge data processing and computing tasks, which consumed much storage and computing resource. There is a high demand for storage and computing resources, which is in contradiction with the limited resources of the airborne hardware. Based on the advantages of cloud computing's computing resource sharing mechanism, this thesis combines robot with cloud computing to research the mobile robot's navigation using visual SLAM. Considering that robot visual SLAM is a computationally intensive task, a cloud computing service platform for robot Los SLAM is constructed. The platform is based on Openstack management platform to build a virtual environment, and uses Docker container technology to complete the design of functional service image. The full use of the platform performance makes the application of sophisticated and advanced algorithms in low-cost robots possible. On this basis, an improved visual SLAM algorithm based on ORB features is designed to realize the robot's effective perception for the unknown environment on the cloud service platform. The results of the algorithm is verified that the service platform can effectively unload the tasks of the robot to the cloud, and reduce the hardware performance requirements of the robot. At the same time, modular functional service forms can expand the functions and use scenarios of robots, break the limits of traditional robots dealing with a single scene, and help to enhance the intelligence of robots.