Towards Real-Time SLAM-Based Orthomosaic Generation for High-Resolution Scientific Multi-Band sUAS Imagery

The increasing availability of high-resolution, multi-spectral cameras for small Unmanned Aerial Systems (sUAS) has enabled detailed aerial mapping for applications such as precision agriculture and environmental monitoring. However, generating orthomosaics from high-resolution imagery presents significant computational challenges, particularly for real-time processing on resource-constrained edge devices. This paper evaluates the feasibility of SLAM-based orthomosaic generation for high-resolution, multi-band sUAS imagery. We systematically analyze trade-offs in resolution scaling, feature extraction strategies, and incremental bundle adjustment techniques, quantifying their effects on accuracy, computational cost, and scalability. Our results show that while global bundle adjustment improves accuracy, localized selection strategies significantly reduce processing time, improving real-time processing performance. Additionally, we discuss the limitations of existing SLAM-based pipelines in handling high-resolution imagery and highlight opportunities to improve performance. By identifying key computational bottlenecks and accuracy trade-offs, this study provides insights for optimizing SLAMbased aerial mapping pipelines for real time scientific grade data analysis.