Advancements in Fisheye Lens Image Stitching for Enhanced ADAS in Automated Vehicles

As Advanced Driver Assistance Systems (ADAS) become integral to the functionality of automated vehicles, the demand for comprehensive and distortion-free visual information is paramount. This survey paper meticulously examines the realm of image stitching utilizing fisheye lenses to cater to the unique needs of ADAS applications. Drawing insights from an extensive analysis of pertinent research papers, this survey delves into the diverse spectrum of techniques devised to enhance fisheye lens image stitching. Key focal points include the rectification of fisheye lens distortion, mitigation of image duplication artifacts, and the seamless synthesis of stitched images. Embracing advancements in computer vision, image processing, and the utilization of OpenGL-based rendering techniques, this survey encapsulates the evolving landscape of image stitching for ADAS in automated vehicles. By dissecting the challenges inherent to distortion correction and object duplication removal, this paper uncovers the progressive strategies employed to elevate the accuracy and visual fidelity of stitched images in the context of ADAS. With a comprehensive synthesis of existing research, this survey serves as an indispensable compass for researchers, engineers, and practitioners navigating the intricate domain of image stitching within the realm of automated vehicle system.