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Localization, mapping, and planning in three-dimensional environments.

机译：在三维环境中进行本地化，映射和规划。

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摘要

Building a map, localizing within the map, and planning using the map are fundamental problems for mobile robotics. Every mobile robotic system must incorporate some type of solution to all three problems.;While the interdependency between mapping and localization is well known as the Simultaneous Localization and Mapping (SLAM) problem, there is a growing understanding in the research community that planning how the robot goes about mapping and exploring an environment (and operating in the environment afterwards) can avoid degenerate conditions and significantly reduce complexity of SLAM. Thus the task of exploring a new environment combines all three problems, since the robot must plan to find actions that reduce uncertainty in both mapping and localization. This combined problem is known as Active SLAM.;Independently, SLAM and planning have been solved in small, two dimensional, structured domains. Robots need to move beyond these simple environments. The challenge is to develop real-time Active SLAM methods that allow robots to explore large, three dimensional, unstructured environments, and allow subsequent operation in these environments over long periods of time.;But scaling up to truly large environments requires a second key insight beyond Active SLAM: to circumvent the scale limitations inherent in SLAM, the world can be divided up into more manageable pieces, or submaps. The SLAM problem then becomes a segmented SLAM problem, which represents the world with a combined metric and topological map, building metric submaps as necessary and refining the topological relationships between submaps.;The contribution of this thesis is a real-time Active SLAM approach that combines a novel evidence grid-based volumetric representation, a robust Rao-Blackwellized particle-filter, a topologically flexible submap segmentation framework, and an integrated stochastic planning method for reducing SLAM uncertainty and predicting possible loop closures based on local map structure. We demonstrate our methods on several robotic platforms in both structured and unstructured large, three dimensional environments.
机译：建立地图，在地图中进行本地化以及使用地图进行规划是移动机器人技术的基本问题。每个移动机器人系统都必须针对所有三个问题采用某种类型的解决方案。虽然众所周知，映射和本地化之间的相互依赖性（即同时定位和制图（SLAM）问题），但研究社区对规划如何机器人着手绘制地图并探索环境（然后在该环境中运行）可以避免退化的情况并显着降低SLAM的复杂性。因此，探索新环境的任务将所有这三个问题结合在一起，因为机器人必须计划找到减少映射和定位不确定性的动作。这个合并的问题称为活动SLAM。独立地，SLAM和计划已在二维的结构化小型域中解决。机器人需要超越这些简单的环境。挑战在于开发实时Active SLAM方法，该方法允许机器人探索大型，三维，非结构化环境，并允许长时间在这些环境中进行后续操作;但是要扩展到真正的大型环境需要第二个关键见解超越Active SLAM：为了规避SLAM固有的规模限制，可以将整个世界划分为更易于管理的部分或子图。然后，SLAM问题变成了分段的SLAM问题，它用度量和拓扑图的组合来表示世界，并根据需要构建度量子图，并细化子图之间的拓扑关系。结合了新颖的基于证据的网格体积表示，强大的Rao-Blackwellized粒子过滤器，拓扑灵活的子图分割框架以及集成的随机规划方法，以减少SLAM不确定性并基于局部图结构预测可能的回路闭合。我们在结构化和非结构化大型三维环境中的多个机器人平台上展示了​​我们的方法。

著录项

  • 作者

    Fairfield, Nathaniel.;

  • 作者单位

    Carnegie Mellon University.;

  • 授予单位 Carnegie Mellon University.;
  • 学科 Engineering Robotics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 148 p.
  • 总页数 148
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:29

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