NSF Young Investigator Award

NSF青年研究员奖

• 批准号: 9257990
• 负责人: David Kriegman
• 金额: $ 28.73万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-15 至 1998-10-05
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9257990&HistoricalAwards=false
• 关键词: NSF; Young; Investigator; Award

This research addresses problems in machine perception and robotics. The work is focused in two areas: recognizing curved objects in images, and mobile robot navigation. Recognizing complex curved 3D objects, whose image features depend strongly on viewpoint, is one of the fundamental problems of computer vision. Objects are modelled by collections of algebraic surfaces and their intersection curves, and the geometric constraints involved in predicting and interpreting the images of these models are represented by sets of polynomial constraints. Techniques from algebraic geometry and robust numerical methods can be used to solve these constraints. Viewpoint dependent features are predicted from an object model and represented as an aspect graph which enumerates all topologically distinct views. Elimination theory is used to relate image measurements to objects, and optimization techniques can be applied to estimate an object's pose; objects are then recognized from a library of models. Problems to be addressed include automatic generation of object models, new object representations, prediction for sensors with limited resolution, computational efficiency, and indexing large data bases. Mobile robots provide a dynamic test-bed for robust computer vision algorithms in an unstructured world. In addition to developing new sensors, research focuses on obtaining the necessary information to perform a task. While some activities are readily achieved through visual serving, others require developing a 3D representation of the environment. Stereo and structure-from-motion provide 3D information from image measurements which can be incorporated into a relational map. This map forms the basis for motion planning; paths revisit previously seen places and strategies for exploring new areas can be determined. Additionally, robot tasks and cultural constraints are described more naturally when objects are recognized within this map.

这项研究解决了机器感知和机器人技术方面的问题。这项工作主要集中在两个领域：识别图像中的弯曲物体和移动机器人导航。复杂曲面三维物体的图像特征高度依赖视点，识别是计算机视觉的基本问题之一。对象由代数曲面及其相交曲线的集合建模，而预测和解释这些模型图像所涉及的几何约束由多项式约束集表示。代数几何技术和鲁棒数值方法可用于解决这些约束。从对象模型预测视点相关的特征，并将其表示为枚举所有拓扑上不同视图的方面图。消去理论用于将图像测量与目标相关联，优化技术可用于估计目标的姿态；然后从模型库中识别对象。需要解决的问题包括对象模型的自动生成、新对象表示、有限分辨率传感器的预测、计算效率以及大型数据库的索引。移动机器人为非结构化世界中的鲁棒计算机视觉算法提供了一个动态测试平台。除了开发新的传感器外，研究的重点是获取执行任务所需的信息。虽然有些活动很容易通过视觉服务实现，但其他活动需要开发环境的3D表示。立体和运动结构从图像测量中提供三维信息，这些信息可以合并到关系地图中。这张地图构成了运动规划的基础；路径重新访问以前看到的地方和探索新领域的策略可以确定。此外，当物体在此地图中被识别时，机器人任务和文化约束的描述更加自然。