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.

人们向过去遇到的地方重新审视，看似遗忘了一天中的时间，天气状况，甚至是进行初次访问的季节。他们主要是通过使用视觉来识别路线和地标并做出导航决策而轻松执行这项任务。机器人可以这样做吗？机器人如何处理其视觉感觉数据并以使其能够识别过去遇到的位置独立于环境条件的方式来构建环境的表示？这些问题仍然挑战当今机器人社区，以及机器人自治的障碍。他们是这项研究建议的核心。 该提案要求资金支持自动机器人视觉导航的研究，这是使移动机器人真正有用的基本要求，以便在家庭和医院，在工作地点以及在诸如地震等自然灾害中的艰苦袭击中胜任地工作。自主导航中的许多研究都采用了愿景作为机器人建立环境地图并在该环境中进行本地化的主要传感器。该提案在视觉机器人导航中解决了一个关键挑战，即，如何以视觉数据来描述环境，以使机器人能够克服仍然阻碍视觉的环境的外观变化。这些变化包括由于一天中的时间，天气，季节以及在重新访问期间机器人视觉传感器的观点变化而导致的照明变化。当机器人长时间地部署在任务中时，这些更改尤其重要。 拟议的研究的长期目标是实现自动驾驶机器人的视觉导航。我们研究的短期目标集中于解决环境各种环境变化不变的环境表示的解决方案。我们实现这一不变代表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照明而变化。因此，如果我们根据其几何特征来表征环境，则可以有效地实现不变的环境表示。其次，在视觉对象检测和识别任务中，最近对深层神经网络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，并对其变化不变的环境创建语义描述。具体而言，我们依靠深度学习来检测和识别场景的组成对象，并且在考虑场景中对象的空间布置时，我们使用这样的语义描述匹配场景。我们的研究将在现有的基准数据集以及用于自动导航研究以及实际物理机器人，陆地和水上的现有基准数据集上进行。