RI: Small: Ultra-Sparsifiers for Fast and Scalable Mapping and 3D Reconstruction on Mobile Robots

RI：小型：用于移动机器人快速、可扩展测绘和 3D 重建的超稀疏器

• 批准号: 1115678
• 负责人: Frank Dellaert
• 金额: $ 44.86万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115678&HistoricalAwards=false
• 关键词: RI; Small; Ultra; Sparsifiers; Fast

This project develops and realizes efficient and large scale mapping and 3D reconstruction on mobile robots. We develop a new optimization paradigm which combines the advantages of both direct and iterative methods by (1) investigating a novel class of optimization methods for robot mapping problems: subgraph-preconditioned conjugate gradients (SPCG) that combine the advantages of direct and iterative methods while minimizing the disadvantages, (2) investigating subgraph preconditioner selection and quality analysis, (3) applying the above techniques to large-scale 3D reconstruction problem mobile robots, and (4) investigating on-line versions of these algorithms. We adapt the SPCG for this setting by incrementally building the graph sparsifier that gives us a good preconditioner.Beyond robotics and vision, we show that similar bounds can be derived for the general problem of approximating distributions. A concrete deliverable of the proposed work is a software package that embeds the new hybrid approach to solving the mapping/reconstruction non-linear optimization problem, and is easily deployable to a wide range of mobile robotic platforms: terrestrial, aerial, underwater, or underground, acting individually or in teams. The robotics research community has access to this technology, which provides great improvement over the capabilities of current mapping/reconstruction software, both in terms of the size of the problem, as well as in terms of speed and online applicability. Finally, at a more local level, this research impacts education of both graduate and undergraduate students at Georgia Tech.

本项目开发并实现了移动的机器人的高效大规模测绘和三维重建。我们开发了一种新的优化范式，它结合了直接和迭代方法的优点，通过（1）研究机器人映射问题的一类新的优化方法：子图预条件共轭梯度法联合收割机了直接法和迭代法的优点，同时最大限度地减少了它们的缺点，（2）研究子图预条件子的选择和质量分析，（3）将上述技术应用于大规模移动的机器人的三维重建问题，（4）研究这些算法的在线版本。我们通过逐步构建图稀疏器来适应SPCG，这给了我们一个很好的预条件子。除了机器人和视觉之外，我们还证明了近似分布的一般问题也可以得到类似的边界。所提出的工作的一个具体的交付是一个软件包，嵌入新的混合方法来解决映射/重建非线性优化问题，并很容易部署到广泛的移动的机器人平台：陆地，空中，水下，或地下，单独或团队行动。机器人研究社区可以使用这项技术，它在问题的大小以及速度和在线适用性方面都大大改善了当前映射/重建软件的功能。最后，在更地方的层面上，这项研究影响了格鲁吉亚理工学院研究生和本科生的教育。