Computer Vision-Based Terrain Sensors for Blind Wheelchair Users

适用于盲人轮椅使用者的基于计算机视觉的地形传感器

• 批准号: 0415310
• 负责人: James Coughlan
• 金额: --
• 依托单位: Smith-Kettlewell Eye Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0415310&HistoricalAwards=false
• 关键词: Computer; Vision; Based; Terrain; Sensors

Approximately one in ten blind persons are confined to wheelchairs, and independent travel is currently next to impossible for this population. Conventional blind wayfinding techniques - cane or guide dog - become extremely difficult or impractical in a wheelchair, requiring great physical dexterity and coordination. The traveler's loss of direct contact with the ground also removes vital feedback. It is easy to miss hazards such as obstacles and drop-offs ahead of or alongside the chair. While some technologies have been developed to aid in wheelchair navigation, they are either restricted to controlled environments or detect only a limited range of obstacles very close to the chair. As a result, independent travel is so difficult that few attempt it, resulting in a widespread lack of awareness of this severely disadvantaged population. The PI's long-term goal is to make independent travel feasible for blind wheelchair users. In this project, he will tackle the problem of sensing terrain features that are relevant to wayfinding in a wheelchair using computer vision technology. Computer vision algorithms for interpreting visual scenes will be developed to infer important visual information, in real time, about nearby terrain, obtained from images collected by cameras mounted to the wheelchair. This information will include the detection of hazards such as obstacles and drop-offs ahead of or alongside the chair, as well as detecting veer, finding curb cuts, finding a clear path, and maintaining a straight course. It will be communicated to the traveler using synthesized speech, audible tones and/or tactile feedback, and is meant to augment rather than replace the information from existing wayfinding skills. The traveler will use this information to control the wheelchair himself/herself (rather than relying on robotic control of the chair). The outcome will be a prototype system that helps prevent veering off the sidewalk by performing the following functions: detect and locate drop-offs and other obstacles; detect and locate curbs and curb cuts; and detect and locate the shoreline (i.e., edge of the sidewalk bordering grass or other terrain, or adjoining a wall), as well as sideslopes.Broader Impact: Beyond the tremendous benefits that the technology resulting from this research may impart to the population of blind wheelchair users, the research will also advance the understanding of computer vision algorithms applied to terrain analysis and interpretation. The unique research environment at Smith-Kettlewell will draw upon the expertise of in-house blind engineers (one of whom is a post-doctoral fellow), who will assist in the design and assessment of the proposed system. In addition, the study will provide an opportunity for minorities and blind persons to gain exposure to science while participating in the gathering of data as research subjects. Finally, the results of the research will be disseminated in several public forums, including demonstrations of the system at local high schools and at the Exploratorium Science Museum of San Francisco.

大约十分之一的盲人只能坐轮椅，对这些人来说，独立旅行目前几乎是不可能的。传统的盲人寻路技术——手杖或导盲犬——在轮椅上变得极其困难或不切实际，需要极大的身体灵活性和协调性。旅行者失去了与地面的直接接触，也失去了重要的反馈。很容易忽略椅子前面或旁边的障碍物和落差等危险。虽然已经开发了一些技术来帮助轮椅导航，但它们要么局限于受控环境，要么只能探测到距离轮椅很近的有限范围的障碍物。因此，独自旅行是如此困难，以至于很少有人尝试这样做，导致人们普遍缺乏对这一严重弱势群体的认识。PI的长期目标是使盲人轮椅使用者能够独立旅行。在这个项目中，他将利用计算机视觉技术解决与轮椅寻路相关的地形特征感知问题。将开发用于解释视觉场景的计算机视觉算法，以从安装在轮椅上的摄像机收集的图像中实时推断有关附近地形的重要视觉信息。这些信息将包括检测椅子前方或旁边的障碍物和跌落等危险，以及检测转向、寻找路缘切口、寻找清晰的路径和保持直线行驶。它将通过合成语音、可听音调和/或触觉反馈传达给旅行者，旨在增强而不是取代现有寻路技能的信息。旅行者将利用这些信息自己控制轮椅（而不是依靠机器人控制轮椅）。结果将是一个原型系统，通过执行以下功能来帮助防止偏离人行道：检测和定位掉落物和其他障碍物；检测和定位路缘和路缘切口；并检测和定位海岸线（即人行道与草地或其他地形接壤的边缘，或毗邻墙壁）以及侧坡。更广泛的影响：除了这项研究产生的技术可能会给盲人轮椅使用者带来巨大的好处之外，这项研究还将促进对应用于地形分析和解释的计算机视觉算法的理解。Smith-Kettlewell独特的研究环境将利用内部盲人工程师（其中一位是博士后）的专业知识，他们将协助设计和评估拟议的系统。此外，这项研究将为少数民族和盲人提供一个机会，让他们在作为研究对象参与数据收集的同时接触科学。最后，研究结果将在几个公共论坛上传播，包括在当地高中和旧金山探索科学博物馆展示该系统。