RI: Small: Robust and Long-Term Visual Mapping and Localization

RI：小型：稳健且长期的视觉映射和本地化

• 批准号: 1318392
• 负责人: John Leonard
• 金额: $ 37.33万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318392&HistoricalAwards=false
• 关键词: RI; Small; Robust; Long; Term

This project develops robust and persistent algorithms for mapping and localization using low-cost visual/depth cameras and inertial sensors. New map representations and algorithms are developed to provide computationally efficient long-term 3D mapping and navigation. Topics of investigation include incremental non-Gaussian inference techniques, dense mapping, change detection in dynamic environments, and semantic understanding. A lack of robustness has been a key shortcoming of previous techniques for localization, and thwarted the development of persistently autonomous mobile robot systems. Extension to multimodal distributions poses significant intellectual difficulties. Dense methods are transforming robotic perception, enabling sophisticated physical interaction with objects, traversal of stairs, and safe maneuvering in cluttered and confined spaces. Whereas most past research in robotic mapping has assumed a static world, the approach being developed in this grant exploits the dynamics of the world to discover information about objects and places. These advances are being tested for robotic and man-portable sensing systems operating in indoor, outdoor, and underwater environments. The expected impacts span a broad range of applications, from robotic manufacturing, medical robotics, agriculture, and space and underwater exploration, in which perception is a key requirement. Other potential spin-offs include human-portable mapping applications in real estate, construction, and facility maintenance, health and safety. MIT Online Robotics Education provides a set of online course materials for core topics in robotics, targeted to a broad audience for high school and college education. Open source software modules provide positioning capabilities for low-cost robots for education and service robotics applications.

该项目使用低成本的视觉/深度相机和惯性传感器开发强大而持久的映射和定位算法。开发了新的地图表示和算法，以提供计算高效的长期3D映射和导航。调查主题包括增量非高斯推理技术，密集映射，动态环境中的变化检测和语义理解。 鲁棒性的缺乏一直是以前的定位技术的一个关键缺点，并阻碍了持续自主移动的机器人系统的发展。扩展到多峰分布带来了重大的智力困难。 密集方法正在改变机器人的感知，使其能够与物体进行复杂的物理交互，穿越楼梯，并在混乱和狭窄的空间中安全操纵。尽管过去大多数机器人地图绘制的研究都假设了一个静态的世界，但这项研究正在开发的方法利用了世界的动态来发现有关物体和地点的信息。 这些进步正在测试机器人和便携式传感系统在室内，室外和水下环境中运行。 预期的影响涵盖了广泛的应用，从机器人制造，医疗机器人，农业，空间和水下探索，其中感知是一个关键要求。 其他潜在的副产品包括在真实的房地产、建筑和设施维护、健康和安全方面的人体便携式地图应用。 MIT Online Robotics Education为机器人技术的核心主题提供了一套在线课程材料，针对高中和大学教育的广泛受众。 开源软件模块为教育和服务机器人应用的低成本机器人提供定位功能。