SGER: Persistent, Autonomous Localization and Mapping of Unstructured Environments

SGER：非结构化环境的持久、自主定位和绘图

• 批准号: 0514639
• 负责人: John Leonard
• 金额: $ 10万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-15 至 2006-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0514639&HistoricalAwards=false
• 关键词: SGER; Persistent; Autonomous; Localization; Mapping

The proposed research will investigate new representation, state estimation, and data association techniques to enable sustained, full 6-DOF motion simultaneous localization and mapping (SLAM) in diverse, large-scale environments. The driving vision is to realize an autonomous robot (and sensor suite) capable of sustained exploration of an arbitrarily large environment mixing indoor and outdoor elements, and human-built and natural scene structure. The work combines (1) a basic science component that will investigate fundamental issues of data representation, generality of environments, and algorithmic scaling behavior; (2) a development component with long-duration excursions with real sensors in a variety of environments using both robotic and human platforms; and (3) a validation and assessment component that will formulate and apply objective assessment metrics. The intellectual merits of the proposed research stem from the scientific challenges of developing persistent, autonomous, large-scale mobile robot navigation and mapping systems. Autonomous navigation is a compelling research goal because it embodies key issues that underlie many difficult problems, including choosing a representation, modeling sensor physics, managing uncertainty, detecting features, and selecting actions in real-time. The broader impacts of achieving a sustained, broad-area SLAM method will form the basis of a number of fundamentally useful capabilities, both from technical and societal standpoints. An enormous number of civil, commercial and military applications use maps, and gain greater utility from maps that are accurate and current with respect to the actual state of the world. Among these applications are vehicle and pedestrian navigation aids, accessibility studies for the disabled, urban planning, rapid emergency response, search and rescue operations, equitable taxation and land-use reform, zoning compliance, geographical information systems, and military planning, training and rehearsal. Autonomous operations, including navigation, mapping, and path planning, are vitally important to mobile robots operating on land, in air, space, underground, and underwater environments. Beyond SLAM, the proposed research can have broad impact on the large class of problems facing the challenge of performing real-time state estimation with highly uncertain data

拟议的研究将研究新的表示，状态估计和数据关联技术，以实现持续的，完整的6自由度运动同时定位和映射（SLAM）在不同的，大规模的环境。 驾驶视觉是实现自主机器人（和传感器套件），能够持续探索混合室内和室外元素的任意大环境，以及人为和自然场景结构。 这项工作结合了（1）一个基础科学组件，将调查数据表示，环境的一般性和算法缩放行为的基本问题;（2）一个开发组件，使用机器人和人类平台在各种环境中使用真实的传感器进行长时间的偏移;以及（3）一个验证和评估组件，将制定和应用客观的评估指标。 拟议研究的智力优势源于开发持久的，自主的，大规模的移动的机器人导航和测绘系统的科学挑战。 自主导航是一个引人注目的研究目标，因为它体现了许多困难问题的关键问题，包括选择表示，建模传感器物理，管理不确定性，检测功能和选择实时行动。从技术和社会的角度来看，实现持续的、大面积的SLAM方法的更广泛的影响将构成许多基本有用能力的基础。 大量的民用、商业和军事应用使用地图，并且从关于世界实际状况的准确和最新的地图中获得更大的效用。 这些应用包括车辆和行人导航辅助设备、残疾人无障碍研究、城市规划、快速应急、搜索和救援行动、公平税收和土地使用改革、分区合规、地理信息系统以及军事规划、培训和演习。 自主操作，包括导航、地图绘制和路径规划，对于在陆地、空中、太空、地下和水下环境中操作的移动的机器人至关重要。 除了SLAM之外，所提出的研究可以对面临高度不确定数据的实时状态估计挑战的大类问题产生广泛的影响