SBIR Phase II: Fast Panoramic Image Capture for Unmanned Aerial Vehicles
SBIR Phase II: Fast Panoramic Image Capture for Unmanned Aerial Vehicles
批准号:
2136737
负责人:
Andrew Kurtz
金额:
$99.95万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-03-15 至 2024-08-31
中文摘要
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英文摘要
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.
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