RUI: Visual Navigation from Circular Feature Sequences

摘要提案0713442 pi: Amy Briggs和Daniel ScharsteinInstitution: Middlebury college标题：RUI：来自圆形特征序列的视觉导航仅从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，因为可以作为导航地标的明确和稳定的特征很难获得。挑战包括世界的丰富几何，不断变化的场景和障碍物，遮挡，照明变化，镜面和透明表面，仅举几例。由于需要处理的数据量庞大，维持世界特征的精确3D模型并将其用于定位和导航非常困难。为了应对这些挑战，本项目提出了一个从二维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现实世界导航任务都在平面上（例如，在单个建筑物楼层行走，在城市中驾驶），因此目标是确定一个足够用于导航的简化特征集，该特征集使用很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种简化的视觉特征模型，该模型允许在平面上进行鲁棒导航，但避免了与提取，建模和匹配真正的3D特征以及解决相关的六自由度运动结构问题相关的大多数困难。这项工作在几个方面推动了基于视觉的导航技术的发展，在机器人和计算机视觉方面都有新的贡献。具体的算法贡献包括对平面运动不变的新特征描述符，允许不匹配特征和顺序反转的快速圆形特征匹配算法，概率拓扑运动规划方法，包括特征检测和识别可靠性的显式建模，以及基于图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，骑自行车者和汽车的基于摄像头的导航助手。该项目也为文理学院的本科生提供了一个接触研究、实验和发现世界的机会。米德尔伯里学院是佛蒙特州农村的一所本科院校，其本科生将积极参与这项研究的所有组成部分。学生研究人员还将与pi一起撰写论文并参加会议。其他学生将从研究活动中受益，通过将当前的研究课题整合到课程中，并通过使用项目增强的实验室设施。研究与教育的紧密结合是pi的核心职业目标。基于之前和现在与本科生合作的经验，机器人和计算机视觉研究非常适合激发和挑战学生。将定期在http:// vision.middlebury.edu/navigation报告该项目的进展情况

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