Onboard Data Management Approach Based on a Discrete Grid System for Multi-UAV Cooperative Image Localization

Onboard image data management and sharing are the foundations for achieving cooperative data processing and analysis in multiple unmanned aerial vehicles (multi-UAVs). However, various challenges, such as the lack of efficient onboard data indices, restrict the development of multi-UAV cooperative applications. Here, we propose a novel and versatile cooperative data management framework based on a discrete grid system for multi-UAV onboard image data. First, we study the image coding methodology employed within the proposed framework. This method transforms original spatiotemporal and attribute information in images into well standardized and structured coded information. Second, we introduce a grid-based onboard image data management (Grid-OIM) approach to facilitate cooperative data management among multi-UAVs using code-based index and query methods. Finally, we applied Grid-OIM to cooperative image localization tasks. Experiments were conducted using an edge-computing platform and an embedded database. The image coding method could process >12 000 images/s while maintaining excellent real-time performance. Moreover, the efficiency of creating and updating the image data index and querying the image data increased by averages of 17.6, 9.3, and 66.1 times, respectively, compared to the image data management method based on R-& lowast;-Tree, highlighting the substantial advantages of this proposed method. These improvements address the demands of indexing and querying highly dynamic onboard image data effectively. Furthermore, the horizontal accuracy of image localization calculated by the cooperative localization method was improved by 43.4%-81.6% compared to that of a single UAV, enhancing reliability. Overall, Grid-OIM presents a feasible and practical solution for multi-UAV cooperative applications.