Monocular Camera Pose Estimation and Calibration System Based on Raspberry Pi

Numerous imaging-based methods have been proposed for artifact monitoring and preservation, yet most rely on fixed-angle cameras or robotic platforms, leading to high cost and complexity. In this study, a portable monocular camera pose estimation and calibration framework is presented to capture artifact images from consistent viewpoints over time. The system is implemented on a Raspberry Pi integrated with a controllable three-axis gimbal, enabling untethered operation. Three methodological innovations are proposed. First, ORB feature extraction combined with a quadtree-based distribution strategy is employed to ensure uniform keypoint coverage and robustness under varying illumination conditions. Second, on-device processing is achieved using a Raspberry Pi, eliminating dependence on external power or high-performance hardware. Third, unlike traditional fixed setups or multi-degree-of-freedom robotic arms, real-time, low-cost calibration is provided, maintaining pose alignment accuracy consistently within three pixels. Through these innovations, a technically robust, computationally efficient, and highly portable solution for artifact preservation has been demonstrated, making it suitable for deployment in museums, exhibition halls, and other resource-constrained environments.