An Efficient and Accurate Solution for the PnPL Problem

Abstract

Camera Pose Estimating from point and line correspondences is critical in various applications, including robotics, augmented reality, 3D reconstruction, and autonomous navigation. Existing methods, such as the Perspective-n-Point (PnP) and Perspective-n-Line (PnL) approaches, offer limited accuracy and robustness in environments with occlusions, noise, or parse feature data. This paper presents a unified solution, Efficient and Accurate Pose Estimation from Point and Line Correspondences (EAPnPL), combining point-based and line-based constraints to improve pose estimation accuracy and computational efficiency, particularly in low-altitude UAV navigation and obstacle avoidance. The proposed method utilizes quaternion parameterization of the rotation matrix to overcome singularity issues and address challenges in traditional rotation matrix-based formulations. A hybrid optimization framework is developed to integrate both point and line constraints, providing a more robust and stable solution in complex scenarios. The method is evaluated using synthetic and real-world datasets, demonstrating significant improvements in performance over existing techniques. The results indicate that the EAPnPL method enhances accuracy and reduces computational complexity, making it suitable for real-time applications in autonomous UAV systems. This approach offers a promising solution to the limitations of existing camera pose estimation methods, with potential applications in low-altitude navigation, autonomous robotics, and 3D scene reconstruction.