NRI: Large-Scale Collaborative Semantic Mapping using 3D Structure from Motion

NRI：使用 Motion 的 3D 结构进行大规模协作语义映射

• 批准号: 1426998
• 负责人: Zsolt Kira
• 金额: $ 39.78万
• 依托单位: Georgia Tech Applied Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426998&HistoricalAwards=false
• 关键词: NRI; Large; Scale; Collaborative; Semantic

The project develops techniques to advance the state of the art in tackling the challenges associated with creating such representations using robots, namely issues related to the scalability and semantic interpretability of such maps. The research activities include advancement of knowledge in multiple fields, such as computer vision, structure from motion, robotics, and semantic mapping. The results have the potential for many societal applications including city planning, asset management, creation of historical records, and support for autonomous driving. The demonstration of the developed theoretical techniques for real-time interaction between humans and robots facilitated by a semantic map enables even greater societal benefit, for example for emergency management, crime prevention, and traffic management. Direct educational impact is anticipated for graduate students and the results are disseminated through both publications and software, allowing the community to leverage the results.This research program advances real-time large-scale distributed semantic mapping of outdoor environments. Specifically, the research team is enabling real-time large-scale semantic mapping by using unsupervised object discovery, obviating the need for large sets of annotated videos for each object category which becomes prohibitive when dealing with hundreds of object categories. The research team frames this process within the structure from motion optimization framework, thereby leveraging geometric and multi-view constraints and features to increase reliability of object track association as well as category clustering. In addition to address scalability, the project develops a distributed, multi-robot system, allowing large teams of air and ground vehicles to cooperatively build a map of large geographic areas in reasonable time frames. Furthermore, the project develops techniques to make the maps more semantically-meaningful and hence interpretable by humans. To accomplish this objective, the research team uses automatic techniques to attach semantic labels to objects discovered in an unsupervised manner. Moreover, humans can interact with the system at multiple levels. Human users can refine both the object categories and semantic labels to increase their accuracy, as well as designate dynamic targets of interest and task robots to track them.

该项目开发了推进艺术的技术，以应对使用机器人创建此类表示相关的挑战，即与此类地图的可伸缩性和语义解释性有关的问题。研究活动包括在多个领域的知识发展，例如计算机视觉，运动，机器人技术和语义映射的结构。结果有可能进行许多社会应用，包括城市规划，资产管理，创建历史记录以及对自动驾驶的支持。语义图促进的人类与机器人实时互动的开发理论技术的演示可以使社会益处更大，例如，紧急管理，预防犯罪和交通管理。预计研究生将对研究生产生直接的教育影响，并通过出版物和软件均能传播结果，从而使社区能够利用结果。这项研究计划推进了实时大规模的户外环境分布式语义映射。具体而言，研究团队通过使用无监督的对象发现来实现实时大规模的语义映射，从而消除了每个对象类别的大量带注释的视频的需求，这些视频在处理数百个对象类别时变得过于望而却步。研究团队将该过程从运动优化框架中构图，从而利用几何和多视图约束和功能来提高对象轨道关联以及类别聚类的可靠性。除了解决可扩展性外，该项目还开发了一个分布式的多机器人系统，使大型的空气和地面车辆可以在合理的时间范围内合作构建大型地理区域的地图。此外，该项目开发了使地图更具语义上的地图的技术，因此可以被人类解释。为了实现这一目标，研究团队使用自动技术将语义标签附加到以无监督方式发现的对象上。此外，人类可以在多个层面上与系统交互。人类用户可以同时完善对象类别和语义标签以提高其准确性，并指定感兴趣的动态目标和任务机器人以跟踪它们。