Visual Models - Application to Situational Awareness

我的研究解决了计算机视觉的表示问题，特别是如何从传感器网络捕获信息，并创建忠实地反映环境随时间变化的计算机模型。在这个提案中有3个具体的子项目。第一个是为与人类合作的机器人助手提供态势感知能力。对象（人+机器）表示为铰接的3D模型，实时跟踪其现实世界的对应对象。科学的重点是如何捕获和模拟动态行为，使系统能够识别或控制每个乘员的活动。继通用汽车之前的研究工作之后，该公司的目标是嵌入具有更强大、更可靠的态势感知能力的机器人助手，从而使实际应用成为可能。如果这一设想成功，未来的装配线将由人机团队合作制造产品，大大提高生产率，使加拿大在全球市场上保持竞争力。

My research addresses the problem of representations for computer vision, specifically how to capture information from a sensor network and create computer models that faithfully reflect an environment over time. There are 3 specific sub-projects within this proposal. The first deals with providing situational awareness for a robotic assistant working in conjunction with a human. Objects (humans + machines) are represented as articulated 3D models that track their real world counterparts in real time. The scientific focus is how to capture and model dynamic behavior so that the system can identify or control the activities of each occupant. Following on previous work with GM research, the goal is to embed robotic assistants with more powerful and reliable situational awareness so that practical implementations become possible. If this vision is successful, future assembly lines will be comprised of human-robot teams collaborating to manufacture products, greatly scaling up productivity and allowing Canada to remain competitive in the global marketplace. The remaining 2 sub-projects are aimed at representations that enable humans to have more precise knowledge about their environments, and robots to be able to function within them. In the first, Sparse Data Models, we look at the problem of how to recover descriptions of an environment with limited sensory ability. The specific example is localizing underground mineral deposits from physical core samples, which are very sparse relative to what one finds in images. We recently have developed some new stochastic modeling techniques that show promise for extending conventional image reconstruction algorithms to these challenging datasets. If successful, this research will lead to more precise algorithms for localizing mineral deposits, which in turn could have a significant impact on mining operations by reducing costs for excavation, transport and processing. Finally, the Deep Learning sub-project is an attempt to leverage impressive technical progress in machine learning to determine representations that are better suited to natural forms. Our focus is on the practical implementation of Hierarchical Generative Models using Convolutional Deep Boltzman machine networks, with the goal of achieving comparable performance with a substantial reduction in complexity. This research will contribute to work with GM in building sensor systems that can operate in off-road environments under adverse weather conditions, as well as resource industry projects that involve identifying structures and landmarks in GPS-deprived environments.