Developing Robot Autonomy via Invariant Representations

通过不变表示开发机器人自主性

• 批准号: RGPIN-2016-04847
• 负责人: Zhang, Hong
• 金额: $ 3.35万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615336
• 关键词: Developing; Robot; Autonomy; via; Invariant

People pay revisits to places encountered in the past seemingly oblivious of the time of the day, the weather conditions or even the season when the initial visits were made. They perform this task effortlessly mostly through the use of their sense of vision to recognize routes and landmarks and make navigational decisions. Can robots do the same? How does a robot process its visual sensory data and build a representation of the environment in such a way that enables it to recognize places that have been encountered in the past independently of the environmental conditions? These questions still challenge the robotics community today, and obstacles to robot autonomy. They are at the heart of this research proposal. This proposal requests funding to support research in visual navigation of autonomous robots, considered a fundamental requirement for a mobile robot to be truly useful, in order to work competently in homes and hospitals, at work sites as well as in areas hard hit by natural disasters such as earthquakes. Much of the research in autonomous navigation has adopted vision as the primary sensor for the robot to build a map of the environment and localize itself within that environment. This proposal tackles a key challenge in visual robot navigation, namely, how to describe an environment from the visual data in such a way that allows a robot to overcome the appearance changes of the environment that still hamper vision. These changes include changes in lighting due to time of the day, weather, season, and changes in the viewpoint of the robot visual sensor during revisits. The changes can be especially significant when robots are deployed on missions over a long period of time. The long-term goal of the proposed research is to achieve autonomous robot visual navigation. The short-term goals of our research focus on solutions to build an environment representation that is invariant to various changes in the environment. Our effort in achieving this invariant representation unfolds on two fronts. First, we recognize that the geometry of an environment does not change with lighting. Therefore, if we characterize an environment in terms of its geometric features, we can effectively achieve an invariant environment representation. Second, we are encouraged by the recent impressive development of deep neural networks in visual object detection and recognition tasks, and we will proceed to use state-of-the-art techniques in deep learning and create a semantic description of the environment that is invariant to its changes. Specifically, we depend on deep learning to detect and recognize constituent objects of a scene, and we match scenes using such a semantic description while taking into account the spatial arrangement of the objects in the scene. Our research will be conducted on both existing benchmark datasets for autonomous navigation research as well as on real physical robots, on land and on water.

人们重游过去去过的地方，似乎忘记了最初访问时的时间、天气状况，甚至季节。他们毫不费力地完成这项任务，主要是通过利用视觉来识别路线和地标并做出导航决策。机器人也能做同样的事吗？机器人如何处理其视觉感官数据并构建环境表示，使其能够独立于环境条件识别过去遇到过的地方？这些问题仍然挑战着当今的机器人界，也是机器人自主化的障碍。它们是本研究计划的核心。 该提案要求提供资金支持自主机器人视觉导航的研究，这被认为是移动机器人真正有用的基本要求，以便能够在家庭、医院、工作场所以及遭受自然灾害严重的地区胜任工作例如地震。自主导航的大部分研究都采用视觉作为机器人的主要传感器，以构建环境地图并在该环境中定位自身。该提案解决了视觉机器人导航中的一个关键挑战，即如何从视觉数据描述环境，从而使机器人能够克服仍然妨碍视觉的环境外观变化。这些变化包括由于一天中的时间、天气、季节而导致的照明变化，以及重访期间机器人视觉传感器的视点变化。当机器人长期部署执行任务时，这些变化可能尤其显着。 该研究的长期目标是实现自主机器人视觉导航。我们研究的短期目标侧重于构建对环境的各种变化不变的环境表示的解决方案。我们为实现这种不变的表示而付出的努力在两个方面展开。首先，我们认识到环境的几何形状不会随着光照而改变。因此，如果我们根据环境的几何特征来表征环境，我们就可以有效地实现不变的环境表示。其次，我们对深度神经网络最近在视觉对象检测和识别任务中取得的令人印象深刻的发展感到鼓舞，我们将继续在深度学习中使用最先进的技术，并创建不变的环境语义描述对其变化。具体来说，我们依靠深度学习来检测和识别场景的组成对象，并使用这种语义描述来匹配场景，同时考虑场景中对象的空间排列。我们的研究将在自主导航研究的现有基准数据集以及陆地和水上的真实物理机器人上进行。