Developing Robot Autonomy via Invariant Representations

人们重访过去遇到的地方，似乎忘记了一天中的时间，天气状况，甚至最初访问的季节。 他们毫不费力地完成这项任务，主要是通过使用他们的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知数据，并以这种方式构建环境表示，使其能够独立于环境条件识别过去遇到的地方？ 这些问题仍然挑战着当今的机器人社区，也是机器人自主性的障碍。 它们是这项研究提案的核心。 该提案要求提供资金，以支持自主机器人视觉导航的研究，这被认为是移动的机器人真正有用的基本要求，以便在家庭和医院，工作场所以及地震等自然灾害严重的地区胜任工作。 自主导航的许多研究都采用视觉作为机器人构建环境地图并在该环境中定位自己的主要传感器。 该建议解决了视觉机器人导航中的一个关键挑战，即如何以允许机器人克服仍然妨碍视觉的环境外观变化的方式从视觉数据描述环境。 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，以及在重新访问期间机器人视觉传感器的视点的变化。 当机器人被长期部署在任务中时，这些变化可能特别显著。 本研究的长期目标是实现机器人的自主视觉导航。我们的研究的短期目标集中在解决方案，以建立一个环境表示，是不变的环境中的各种变化。 我们在实现这一不变表示展开在两个方面的努力。 首先，我们认识到环境的几何形状不会随着照明而改变。 因此，如果我们根据环境的几何特征来描述环境，我们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，并创建对环境变化不变的语义描述。 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。我们的研究将在现有的自主导航研究基准数据集以及陆地和水上的真实的物理机器人上进行。

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.