Abstract
In this paper, we present a novel red-green-blue-depth simultaneous localization and mapping (RGB-D SLAM) algorithm based on cloud robotics, which combines RGB-D SLAM with the cloud robot and offloads the back-end process of the RGB-D SLAM algorithm to the cloud. This paper analyzes the front and back parts of the original RGB-D SLAM algorithm and improves the algorithm from three aspects: feature extraction, point cloud registration, and pose optimization. Experiments show the superiority of the improved algorithm. In addition, taking advantage of the cloud robotics, the RGB-D SLAM algorithm is combined with the cloud robot and the back-end part of the computationally intensive algorithm is offloaded to the cloud. Experimental validation is provided, which compares the cloud robotic-based RGB-D SLAM algorithm with the local RGB-D SLAM algorithm. The results of the experiments demonstrate the superiority of our framework. The combination of cloud robotics and RGB-D SLAM can not only improve the efficiency of SLAM but also reduce the robot’s price and size.
Highlights
Simultaneous localization and mapping (SLAM) is an important research topic in the field of autonomous mobile robots, and it is the key for mobile robots to achieve autonomous navigation and perform tasks in an unknown environment, which embodies the robot’s perception ability and intelligence level
(3) We present a novel RGB-D SLAM algorithm based on cloud robotics, which offloads intensive computational tasks to the cloud
The visual RGB-D SLAM method proposed in this paper is mainly based on a distributed framework, which takes expensive computing and storage tasks as a service in the cloud, while those tracking tasks with high real-time requirements can be used as local client services
Summary
Simultaneous localization and mapping (SLAM) is an important research topic in the field of autonomous mobile robots, and it is the key for mobile robots to achieve autonomous navigation and perform tasks in an unknown environment, which embodies the robot’s perception ability and intelligence level. Compared with the traditional SLAM technology, RGB-D SLAM has better real-time performance and less computation for data analysis than traditional SLAM, which is more suitable for the construction of a 3D environment map. To solve the disadvantages of the original RGB-D SLAM, such as low efficiency and poor real-time performance, we have improved several key links of the RGB-D. Cloud robot technology provides a new direction for solving the problems of the RGB-D SLAM algorithm, such as intensive computing and huge data. This paper combines these two technologies to provide a new solution for improving the efficiency and real-time performance of the RGB-D SLAM algorithm.
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