RUI: Visual Navigation from Circular Feature Sequences

RUI：圆形特征序列的视觉导航

• 批准号: 0713442
• 负责人: Amy Briggs
• 金额: $ 32万
• 依托单位: Middlebury College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713442&HistoricalAwards=false
• 关键词: RUI; Visual; Navigation; Circular; Feature

AbstractProposal 0713442 PIs: Amy Briggs and Daniel ScharsteinInstitution: Middlebury CollegeTitle: RUI: Visual Navigation from Circular Feature SequencesNavigating the world from visual input alone is still one of the greatest challenges for mobile robots. Vision provides the richest but also most difficult input, since unambiguous and stable features that can serve as landmarks for navigation are hard to obtain. Challenges include the rich geometry of the world, changing scenes and obstacles, occlusion, lighting changes, specular and transparent surfaces, to name a few. Maintaining an accurate 3D model of world features and using them for localization and navigation is difficult due to the sheer amount of data to be processed.To face these challenges, this project presents a framework for navigation from one-dimensional circular feature sequences extracted from 2D panoramic images. Since many real-world navigation tasks of mobile entities are in the plane (e.g., walking in a single building floor, driving in the city), the goal is to identify a reduced feature set sufficient for navigation that uses little storage space and can be processed quickly.The key motivation for this work is to develop a simplified visual feature model that allows robust navigation in the plane but avoids most of the difficulties associated with extracting, modeling, and matching true 3D features and solving the associated six degree-of- freedom structure-from-motion problem. This work advances the state of the art in vision-based navigation on several fronts, with novel contributions in both robotics and computer vision. Specific algorithmic contributions include new feature descriptors invariant to planar motion, fast circular feature matching algorithms that allow both unmatched features and ordering reversals, probabilistic topological motion planning methods that include explicit modeling of the reliability of feature detection and identification, and strategies for graph-based world modeling and selective feature storage.The proposed techniques have wide applicability, ranging from autonomous robot navigation in unknown environments to camera-based navigation aides for pedestrians, bicyclists, and cars. The project also provides an opportunity to expose undergraduates at a liberal- arts college to the world of research, experimentation, and discovery.Undergraduate students at Middlebury College - an undergraduate institution in rural Vermont - will be actively involved in all components of this research. The student researchers will also join the PIs in authoring papers and attending conferences. Other students will benefit from the research activities through the integration of current research topics into the curriculum and through use of the lab facilities enhanced by the project. Tight integration of research and education is a central career goal of the PIs. Based on experience gained in prior and current collaboration with undergraduates, robotics and computer vision research is ideally suited to excite and challenge students.Progress on this project will be regularly reported at http:// vision.middlebury.edu/navigation

摘要建议0713442 PI：艾米·布里格斯和丹尼尔·沙尔斯坦机构：米德尔伯里学院标题：RUI：圆形特征序列的视觉导航仅通过视觉输入导航世界仍然是移动机器人面临的最大挑战之一。视觉提供了最丰富但也是最困难的输入，因为很难获得可以作为导航地标的明确和稳定的特征。挑战包括世界丰富的几何图形、不断变化的场景和障碍物、遮挡、光线变化、镜面反射和透明表面，仅举几例。由于要处理的数据量太大，维护精确的世界特征3D模型并将其用于定位和导航是困难的。针对这些挑战，本项目提出了一个从2D全景图像中提取的一维圆形特征序列进行导航的框架。由于移动实体的许多真实导航任务都在平面上(例如，在建筑物的单个楼层中行走，在城市中行驶)，目标是识别一个足够用于导航的精简特征集，该特征集使用较少的存储空间并且可以快速处理，这项工作的主要动机是开发一个简化的视觉特征模型，该模型允许在平面上进行健壮导航，并且避免了与提取、建模和匹配真实三维特征相关的大部分困难，并解决了相关的六自由度结构运动问题。这项工作在几个方面推进了基于视觉的导航的最新水平，在机器人学和计算机视觉方面都做出了新的贡献。具体的算法贡献包括新的平面运动不变特征描述符，允许不匹配特征和顺序反转的快速圆形特征匹配算法，包括明确建模特征检测和识别可靠性的概率拓扑运动规划方法，以及基于图形的世界建模和选择性特征存储策略。所提出的技术具有广泛的适用性，从未知环境中的自主机器人导航到行人、骑自行车和汽车的基于相机的导航辅助。该项目还提供了一个机会，让文科大学的本科生接触研究、实验和发现的世界。米德尔伯里学院的本科生将积极参与这项研究的所有组成部分。米德尔伯里学院是佛蒙特州农村的一所本科院校。学生研究人员还将与私人投资机构一起撰写论文和参加会议。其他学生将从研究活动中受益，方法是将当前的研究主题整合到课程中，并通过使用该项目增强的实验设施。研究和教育的紧密结合是私人投资机构的核心职业目标。根据以前和现在与本科生合作获得的经验，机器人学和计算机视觉研究非常适合激发和挑战学生。该项目的进展情况将定期在http：//vision.midlebury.edu/导航上报告