Data Fusion Algorithm for Heterogeneous 2D and Infrared Sensors

In this article, we explore a data fusion algorithm for heterogeneous sensors combining 2D visual and infrared data, with implementation using the Extended Kalman Filter (EKF). We provide detailed explanations of the algorithm's workflow, starting from the initial data collection phase through comprehensive data processing and analysis procedures, culminating in visualization of fusion results. The implementation typically involves sensor calibration matrices, coordinate transformation functions, and measurement update equations that handle the nonlinear relationships between different sensor modalities. We examine practical applications and future development directions, including potential implementations in robotics and autonomous vehicle systems where heterogeneous sensor fusion is critical for robust perception. The algorithm's core functionality includes Jacobian matrix calculations for linearization and covariance propagation for uncertainty management. By the conclusion of this article, readers should gain thorough understanding of the algorithm's significance, practical application value, and implementation methodologies for processing multi-sensor data in real-world scenarios.