Implementation of a cloud-based visual simultaneous localization and mapping method for low-cost robots

Aiming at the high computing performance requirements of visual Simultaneous localization and mapping (SLAM) on robots, this article proposes a cloud-based visual SLAM method for low-cost robots which have limited computing performance. In this method, the monocular ORB-SLAM algorithm is segmented, and the task of key point extraction is completed by the robot. The cloud completes pose calculation and map construction through the feature values sent by the robot. Finally, the hardware and software platforms are designed for verification. The experimental results show that the robot based on ESP32-S3 module can meet the computing power requirements of feature extraction, and the cloud can also smoothly locate and build maps through the feature point data from the robot.