RI: Small: Vision-Based Mobile Manipulation and Navigation

RI：小型：基于视觉的移动操纵和导航

• 批准号: 1017134
• 负责人: Gaurav Sukhatme
• 金额: $ 44.93万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017134&HistoricalAwards=false
• 关键词: RI; Small; Vision; Based; Mobile

This project focuses on tackling a critical barrier to long-term autonomy for robotic systems, namely the lack of theoretically well-founded self-calibration methods for inertial and vision-based sensors, commonly found on sophisticated robots. The project is motivated by the vision of power-up-and-go robotic systems that are able to operate autonomously for long periods without requiring tedious manual sensor calibration. The research team addresses this problem in the context of vision-based mobile manipulation and navigation. The core foci of the work are: 1. the development of a unified mathematical theory of anytime, automatic calibration for visual-inertial systems, and 2. an experimental characterization of the resulting algorithms with state-of-the-art, sophisticated robots of significant diversity (humanoids performing mobile manipulation and autonomous ground vehicles navigating outdoors). Inertial sensing is critically important for humanoid balance control, while visual sensing relates the 3D world to the robot's body coordinates thereby enabling manipulation. In the case of autonomous ground vehicles, monocular and stereo camera calibration is still commonly performed manually using a known calibration target. The project obviates the need for this requirement. The expected outcomes of the project are: 1. a theoretical foundation for humanoid robots to function autonomously in unstructured environments over significant periods of time, and 2. new navigation algorithms for ground vehicles allowing them to see further with greater acuity. The project explicitly incorporates undergraduate research in cooperation with an REU site currently operational at the USC Computer Science Department.

该项目的重点是解决机器人系统长期自主的关键障碍，即缺乏理论上有充分依据的惯性和基于视觉的传感器自校准方法，这些方法通常在复杂的机器人上发现。该项目的动力来自于能够长时间自主运行而不需要繁琐的手动传感器校准的上电即走机器人系统的愿景。研究小组在基于视觉的移动的操纵和导航的背景下解决了这个问题。工作的核心重点是：1。发展了统一的数学理论，用于视觉惯性系统的随时自动校准; 2.最先进的，复杂的机器人的显着多样性（类人执行移动的操纵和自主地面车辆导航户外）的实验特性的算法。惯性传感对于人形平衡控制至关重要，而视觉传感将3D世界与机器人的身体坐标联系起来，从而实现操纵。 在自主地面车辆的情况下，单目和立体相机校准通常仍然使用已知的校准目标手动执行。该项目消除了这一需求。该项目的预期成果是：1。一个理论基础的人形机器人功能自主在非结构化的环境中，在相当长的一段时间，和2。新的地面车辆导航算法，使他们能够看到更远的更大的敏锐度。该项目明确纳入了本科生的研究与REU网站目前在南加州大学计算机科学系运作的合作。