SBIR Phase II: Epipolar-Plane Imaging for Robot 3D Vision

SBIR 第二阶段：机器人 3D 视觉的极面成像

• 批准号: 2242216
• 负责人: Henry Baker
• 金额: $ 99.94万
• 依托单位: EPIImaging, LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242216&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Epipolar; Plane

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project seeks to improve robotic interactions with the humans. Currently, robots are involved in large sectors of society including logistics, manufacturing, autonomous navigation, video communication, remote supervision of complex mechanical maintenance/repair tasks, support in battlefields and disasters, and interactions in various training, educational, and interventional scenarios including telemedicine. This technology may offer more effective automation in the workplace through higher quality 3D sensing, greater precision visualization and increased worker quality of life. The technology addresses precision and reliability of passive 3D scene measurements. This Small Business Innovation Research (SBIR) Phase II project addresses the acquisition of reliable and precise three-dimensional representations of a scene from passively acquired image data for use in navigation, grasping, manipulation, and other operations of autonomous systems in unrestricted three-dimensional spaces. This technology has been a long-standing challenge in the computer vision field, with many efforts providing adequate solutions under certain conditions, but lacking applicability across a breadth of applications. Other approaches typically deliver inaccurate results where there are, for example, repeated structures in the view, thin features, a large range in depth, or where structures align with aspects of the capture geometry. Based on the matching of features across images, current technologies fail when features have similar appearance. This technology removes the uncertainty of this process through a low-cost use of over-sampling, using a specific set of additional perspectives to replace the “matching” with deterministic linear filtering. Increasing the reliability and precision of 3D scene measurements will open new opportunities for robotic interactions with the world. Success in this project will advance the underlying light-field technology to broader application areas where human-in-the-loop operations using artificial reality/virtual reality (AR/VR) or mixed reality (such as remote collaboration and distance interaction) depend on accurate and responsive visualization and scene modeling, reducing influences of vestibular and proprioceptive mismatch that can cause disruptive effects such as nausea.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.

这项小型企业创新研究（SBIR）II阶段项目的广播/商业影响旨在改善与人类的机器人互动。目前，机器人参与了社会的大型部门，包括物流，制造，自动导航，视频通信，对复杂机械维护/维修任务的远程监督，战场和灾难的支持以及包括远程医疗在内的各种培训，教育和介入的场景中的互动。通过更高质量的3D传感，更高的精度可视化和工人的生活质量提高，这项技术可以在工作场所提供更有效的自动化。该技术解决了被动3D场景测量的精度和可靠性。这项小型企业创新研究（SBIR）第二阶段项目介绍了从被动获取的图像数据中获取可靠，精确的三维表示，以在不受限制的三维空间中用于导航，掌握，操作，操作以及自动级系统的其他操作。在计算机视觉领域，这项技术一直是一个长期的挑战，许多努力在某些条件下提供了足够的解决方案，但在广泛的应用程序中缺乏适用性。其他方法通常会产生不准确的结果，例如，在视图中有重复的结构，薄特征，深度范围很大，或者结构与捕获几何形状的各个方面保持一致。基于功能跨图像的匹配，当功能具有相似的外观时，当前技术失败。这项技术通过低成本的过度采样来消除此过程的不确定性，并使用一组特定的其他视角来替换确定性线性过滤的“匹配”。提高3D场景测量的可靠性和精度将为与世界的机器人互动打开新的机会。该项目的成功将把潜在的光场技术推向更广泛的应用领域，在该领域中，人工现实/虚拟现实（AR/VR）或混合现实（例如远程协作和距离互动）取决于准确而响应的可视化和场景建模，并降低了依据的影响，并造成了促进性效应的效果。诚实地通过评估来诚实地使用基金会的智力优点和更广泛的影响审查标准。