Spatiotemporal stereo vision for space and terrestrial robotics

我和我的合作者进行的研究是在计算机视觉的一般领域，试图赋予机器视觉。从理论的角度来看，这是一项重要的奋进，因为它关系到复杂信息处理中的基本问题，并且可能产生影响我们对自然系统中视觉信息处理的理解的结果（例如，人），也。从实践的角度来看，可以看到的机器有可能以比目前更自然和有用的方式与人类互动。本特别提案所包含的研究代表了加拿大学术界（约克大学）和工业界（MDA）之间的合作努力。它涉及从双目（配对）视频摄像机恢复环境的三维模型。我们认为双目相机，类似于人类和其他动物的双眼设计，提供的多个视图提供了一个原则性的几何方法来恢复环境的三维结构。我们考虑视频，因为它允许摄像机扫出广泛的区域覆盖范围，并捕捉环境的动态方面。这项研究在三个重要方面对加拿大做出了贡献。首先，它推进了计算机视觉学科，因为我们揭示了图像信息的基本属性，并开发了相应的算法，以纳入视觉机器;这使加拿大能够在科学和技术的关键领域成功竞争。其次，它为学生提供调查、计算机视觉以及更广泛的计算机科学等重要领域的实践经验和技能。学生继续在科学，技术，教学和其他地方的职业生涯，与他们一起参与研究的丰富。第三，它为实际利用图像数据的新方法铺平了道路。感兴趣的应用包括个人手持3D建模器，用于空间和地面部署的实时机器人技术以及自动车辆导航。

The research that my collaborators and I carry out is in the general area of computer vision, the attempt to endow machines with a sense of sight. From a theoretical point of view this is an important endeavor as it bears on fundamental issues in complex information processing, and may yield results that bear on our understanding of visual information processing in natural systems (e.g., humans), as well. From a practical point of view, machines that can see have the potential to interact with humans in a much more natural and useful fashion than is currently the case. The research encompassed in this particular proposal represents a collaborative effort between Canadian academia (York University) and industry (MDA). It is concerned with recovering three-dimensional models of the environment from binocular (paired) video cameras. We consider binocular cameras as, similar to the two-eyed design of humans and other animals, the afforded multiple views provide a principled geometric approach to recovering three-dimensional structure of the environment. We consider video as it allows the cameras to sweep out wide area coverage and to capture dynamic aspects of the environment. This research contributes to Canada in three important ways. First, it advances the discipline of computer vision as we expose basic properties of image information and develop corresponding algorithms for incorporation into vision machines; this allows Canada to compete successfully in a critical area of science and technology. Second, it provides students with hands on experience and skills in vital areas of investigation, computer vision and, more generally, computer science. Students go on to make careers in science, technology, teaching and elsewhere, taking with them the enrichment of having been involved in research. Third, it paves the way for novel ways to practically exploit image data. Applications of interest include personal hand-held 3D modelers, real-time robotics for space and terrestrial deployment and automated vehicle navigation.