SBIR Phase II: Fast Panoramic Image Capture for Unmanned Aerial Vehicles

这个小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力是使无人机(UAV)在房地产、农业、建筑、能源、娱乐、石油天然气和新闻等行业得到更广泛的采用。该项目开发具有无缝360度成像能力的先进无人机摄像系统，以增加飞行时间和成像能力。最终用户受益于减少的劳动力需求和原本无法访问的偏远地区的实时高保真图像。工业检查、作物管理、田间勘测和现场安全通常是通过部署人员到田间进行的，这往往会造成劳动力瓶颈和潜在的危险情况。无人机在这些应用中的应用可能受到以下因素的限制：图像处理对无人机使用消耗太多电力，图像呈现延迟影响导航能力，或者图像伪影模糊了与市场需求相关的细节。这些限制都可以通过这里开发的相机技术来克服。这个小型企业创新研究(SBIR)第二阶段项目开发了一种轻型、无视差、360度全景相机系统，其创新的光学设计基于相邻相机的多面体形状配置。每台摄像机的视野与下一台摄像机紧密相连，但不会重叠，从而能够无缝且计算简单地捕捉360度全景图像，同时限制摄像机之间的盲区和内容丢失。这种方法消除了广角镜头和多摄像机系统带来的拼接误差和图像失真，并降低了360度图像捕获的计算需求。因此，在无人机(UAV)应用中，低能量边缘计算在对飞行时间或能量使用的影响最小的情况下实时呈现图像。此外，该项目还推进了针对无人机重量和大小进行优化的系统设计，以及针对运动学空间框架的改进概念，其中摄像头可以集成到一起形成强大的多摄像头系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact / commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to enable wider adoption of unmanned aerial vehicles (UAVs) across industries such as real estate, agriculture, construction, energy, entertainment, oil & gas, and journalism. The project develops advanced UAV camera systems with seamless 360-degree imaging capabilities to increase flight time and the imaging capability. End users benefit from reduced labor demands and real-time high-fidelity images of remote areas otherwise inaccessible. Industrial inspections, crop management, field surveying, and site security are most often conducted by deploying humans into the field - often creating labor bottlenecks and potentially dangerous situations. Adoption of UAVs for these applications can be limited by image processing that consumes too much power for UAV use, delayed image presentation affecting navigation capabilities, or image artifacts that obscure the details pertinent to market needs. These limitations can all be overcome with the camera technologies developed here.This Small Business Innovation Research (SBIR) Phase II project develops a lightweight, parallax-free, 360-degree panoramic camera system with an innovative optical design based on a polyhedron-shaped configuration of adjacent cameras. Each camera's field of view closely abuts but does not overlap with the next, enabling seamless and computationally simplistic capture of images from 360-degree views, while limiting blind regions and lost content between cameras. This approach eliminates the stitching errors and image distortion associated with wide angle lenses and multi-camera systems, and reduces the computational demand for 360-degree image capture. As a result, low energy edge-computing presents images in real time with minimal impact on flight time or energy use in unmanned aerial vehicles (UAV) applications. Furthermore, this project advances system designs optimized for UAV weight and size, as well as improved concepts for kinematic space frames, in which the cameras can be integrated to form robust multi-camera systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.