Learning Minimal Representations for Visual Navigation and Recognition II

学习视觉导航和识别的最小表示 II

• 批准号: 0214383
• 负责人: Michael Tarr
• 金额: --
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0214383&HistoricalAwards=false
• 关键词: Learning; Minimal; Representations; Visual; Navigation

Consider how you find your way to the grocery store or learn the layout of a new mall, or how scientists might build a robot that can be dropped on Mars to navigate its surface. People, animals, and robots must navigate complex environments, but different strategies are applied in different situations. One may get to the grocery store by dead reckoning like ants, following landmarks like honeybees, or one can use a precise "memory map" of the environment. Moreover, clever combinations of strategies can make it easier to find the way. The present research effort specifically explores how these strategies are integrated to allow robust visual navigation.With NSF support, Dr. Michael Tarr and Dr. William Warren study how people learn the layout of new environments, the geometry of the resulting spatial knowledge, and how it is used to navigate. The uniqueness of their approach is to study actual navigation behavior, as people actively walk through a computer-generated virtual environment (the VENLab - see http://www.cog.brown.edu/Research/ven_lab/ ). Participants wear a head-mounted virtual reality display and walk freely in a 40 x 40 ft area. Their movements are recorded by a tracking system in the ceiling. After participants learn the layout, the environment can be surreptitiously changed, and they must, in effect, find a new route to the grocery store. By distorting the virtual world or changing the properties of landmarks, these scientists determine the navigational strategies people use and how they rely on routes, landmarks, and the geometry of space.

想想你如何找到去杂货店的路，或者了解一个新购物中心的布局，或者科学家如何建造一个可以降落在火星上的机器人来导航它的表面。 人、动物和机器人必须在复杂的环境中导航，但在不同的情况下应用不同的策略。 一个人可以像蚂蚁一样通过航位推算到达杂货店，像蜜蜂一样跟随地标，或者一个人可以使用精确的环境“记忆地图”。 此外，策略的巧妙组合可以使找到方法更容易。 目前的研究工作专门探讨了如何将这些策略整合起来，以实现强大的视觉导航。在NSF的支持下，Michael Tarr博士和William Warren博士研究了人们如何学习新环境的布局，由此产生的空间知识的几何形状，以及如何将其用于导航。 他们的方法的独特之处在于研究实际的导航行为，因为人们积极地走过计算机生成的虚拟环境（VENLab -参见http://www.cog.brown.edu/Research/ven_lab/）。 参与者佩戴头戴式虚拟现实显示器，在40 x 40英尺的区域内自由行走。 他们的行动被天花板上的跟踪系统记录下来。 在参与者了解布局后，环境可以被悄悄地改变，实际上，他们必须找到一条新的路线去杂货店。 通过扭曲虚拟世界或改变地标的属性，这些科学家确定了人们使用的导航策略，以及他们如何依赖路线，地标和空间的几何形状。

项目成果

Wormholes in virtual space: From cognitive maps to cognitive graphs

虚拟空间中的虫洞：从认知图到认知图

• DOI: 10.1016/j.cognition.2017.05.020
• 发表时间: 2017
• 期刊: Cognition
• 影响因子: 3.4
• 作者: Warren, William H.;Rothman, Daniel B.;Schnapp, Benjamin H.;Ericson, Jonathan D.
• 通讯作者: Ericson, Jonathan D.