Dual-Vehicle Heterogeneous Collaborative Scheme with Image-Aided Inertial Navigation

The Global Positioning System (GPS) has revolutionized navigation in modern society. However, the susceptibility of GPS signals to interference and obstruction poses significant navigational challenges. This paper introduces a GPS-denied method based on scene image coordinates instead of real-time GPS signals. Our approach harnesses advanced image feature-recognition techniques, employing an enhanced scale-invariant feature transform algorithm and a neural network model. The recognition of prominent scene features is prioritized, thus improving recognition speed and precision. The GPS coordinates are extracted from the best-matching image by juxtaposing recognized features from the pre-established image database. A Kalman filter facilitates the fusion of these coordinates with inertial measurement unit data. Furthermore, ground scene recognition cooperates with its aerial counterpart to overcome specific challenges. This innovative idea enables heterogeneous collaboration by employing coordinate conversion formulas, effectively substituting traditional GPS signals. The proposed scheme may include military missions, rescues, and commercial services as potential applications.