A Novel Method to Improve the Efficiency and Performance of Cloud-Based Visual Simultaneous Localization and Mapping

Since Visual Simultaneous Localization and Mapping (VSLAM) inherently requires intensive computational operations and consumes many hardware resources, these limitations pose challenges to implementing the entire VSLAM architecture within limited processing power and battery capacity. This paper proposes a novel solution to improve the efficiency and performance of exchanging data between the unmanned aerial vehicle (UAV) and the cloud server. First, an adaptive ORB (oriented FAST and rotated BRIEF) method is proposed for precise tracking, mapping, and re-localization. Second, efficient visual data encoding and decoding methods are proposed for exchanging the data between the edge device and the UAV. The results show an improvement in the trajectory RMSE and accurate tracking using the adaptive ORB-SLAM. Furthermore, the proposed visual data encoding and decoding showed an outstanding performance compared with the most used standard JPEG-based system over high quantization ratios.