Real-time processing of UAV imagery is crucial for applications requiring urgent geospatial information, such as disaster response, where rapid decision-making and accurate spatial data are essential. However, processing high-resolution imagery in real time presents significant challenges due to the computational demands of feature extraction, matching, and bundle adjustment (BA). Conventional BA methods either downsample images, sacrificing important details, or require extensive processing time, making them unsuitable for time-critical missions. To overcome these limitations, we propose a novel real-time BA framework that operates directly on full-resolution UAV imagery without downsampling. Our lightweight, onboard-compatible approach divides each image into user-defined patches (e.g., N×N grids, default 150×150 pixels) and dynamically tracks them across frames using UAV GNSS/IMU data and a coarse, globally available digital surface model (DSM). This ensures spatial consistency for robust feature extraction and matching between patches. Overlapping relationships between images are determined in real time using UAV navigation system, enabling the rapid selection of relevant neighbouring images for localized BA. By limiting optimization to a sliding cluster of overlapping images, including those from adjacent flight strips, the method achieves real-time performance while preserving the accuracy of global BA. The proposed algorithm is designed for seamless integration into the DLR Modular Aerial Camera System (MACS), supporting large-area mapping in real time for disaster response, infrastructure monitoring, and coastal protection. Validation on MACS datasets with 50MP images demonstrates that the method maintains precise camera orientations and high-fidelity mapping across multiple strips, running full bundle adjustment in under 2 seconds without GPU acceleration.