CAREER: Geometric and Appearance Based Methods for Model Acquisition

该提案的目标是创造一个研究环境，并开发一个专门研究和开发原型的课程。重点将是通过视觉感知捕捉对象和环境的表示的基本问题。目标包括增强机器人代理的自主性，赋予人类额外的能力，并提高人类和机器人交互的水平。 所提出的自适应表示将捕获的几何形状和外观的环境在不同层次的细节，并将获得鲁棒性适当利用上下文和任务相关的约束。 这个视觉系统的两个基本属性，区分我们的方法从以前现有的方法是（1）它在扩展的动态3D环境中的连续操作和（2）它的能力，以适应取决于环境和手头的任务。所提出的研究的智力价值将在（1）推进最先进的在线方法，用于估计摄像机运动和场景结构;（2）独特的学习基于外观的表示的对象和环境从连续的视频流和（3）在视觉引导任务的背景下所获得的模型的集成。广泛的影响包括促进对生物系统的理解，为构建人工系统提供重要指导，并与新的技术趋势一起，将产生新的应用。开发的功能将使更复杂的机器人任务在结构化程度较低的环境中，并推进人类机器人互动-这些功能对于部署服务中的（人形）机器人代理至关重要。 它们还可以增强人类的导航和规划能力，并可能影响我们的生活和衰老方式。

The objective the proposal is to create a research environment and develop a curriculum dedicated to studying and developing prototypes of seeing systems. The emphasis will be on the fundamental issue of capturing representations of objects and environments by means of visual sensing. Goals include enhancing autonomy of robotic agents, empowering humans with additional capabilities and advancing the level of human and robot interaction. The proposed adaptive representations will capture both the geometry and the appearance of the environment at various levels of detail and will be acquired robustly by properly exploiting the context and task related constraints. The two essential properties of this visual system that distinguish our approach from previously existing methods is (1) its continuous operation in extended dynamic 3D environments and (2) its capability to adapt depending on the environment and task at hand. The intellectual merit of the proposed research will be in (1) advancing the state of the art in on-line methods for estimation of camera motion and scene structure; (2) unique learning of appearance based representations of objects and environments from a continuous video stream and (3) in the integration of the acquired models in the context of visually guided tasks. The broad impact includes advancing the understanding of biological systems, providing important guidance in building artificial systems, and together with new technological trends, will give rise to novel applications. The developed capabilities will enable more complex robotic tasks in less structured environments and advance human robot interaction -- features that are critical for deployment of (humanoid) robotic agents in service. They can also augment human navigation and planning capabilities and potentially affect the way we live and age.