Stitching in Fixed Wide-field-of-view Scenarios

In certain scenarios, due to the limitation of a single camera in capturing the entire scene, we need to employ multiple cameras to gather information and seamlessly fuse multiple local images into a panoramic image, showcasing significant potential across a wide range of application domains. This paper focuses on the theory and practice of image stitching in fixed wide-field-of-view scenarios. Firstly, we delve into the fundamental principles and methods of image stitching, including feature extraction, feature matching, and image blending. Secondly, we provide an overview and analysis of current state-of-the-art image stitching algorithms, exploring their advantages and limitations, particularly in the context of fixed field of view. To address the common challenges in fixed wide-field-of-view image stitching, this paper proposes a novel stitching algorithm based on grid distortion. By utilizing multiple sets of scene images to guide the new image stitching process, we achieve higher precision in image matching and blending while retaining fine details. Experimental results demonstrate the effectiveness of our approach in a self-constructed scene.