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Long-Term Simultaneous Localization and Mapping in Dynamic Environments.

机译：动态环境中的长期同时本地化和映射。

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摘要

One of the core competencies required for autonomous mobile robotics is the ability to use sensors to perceive the environment. From this noisy sensor data, the robot must build a representation of the environment and localize itself within this representation. This process, known as simultaneous localization and mapping (SLAM), is a prerequisite for almost all higher-level autonomous behavior in mobile robotics. By associating the robot's sensory observations as it moves through the environment, and by observing the robot's ego-motion through proprioceptive sensors, constraints are placed on the trajectory of the robot and the configuration of the environment. This results in a probabilistic optimization problem to find the most likely robot trajectory and environment configuration given all of the robot's previous sensory experience. SLAM has been well studied under the assumptions that the robot operates for a relatively short time period and that the environment is essentially static during operation. However, performing SLAM over long time periods while modeling the dynamic changes in the environment remains a challenge.;The goal of this thesis is to extend the capabilities of SLAM to enable long-term autonomous operation in dynamic environments. The contribution of this thesis has three main components: First, we propose a framework for controlling the computational complexity of the SLAM optimization problem so that it does not grow unbounded with exploration time. Second, we present a method to learn visual feature descriptors that are more robust to changes in lighting, allowing for improved data association in dynamic environments. Finally, we use the proposed tools in SLAM systems that explicitly models the dynamics of the environment in the map by representing each location as a set of example views that capture how the location changes with time.;We experimentally demonstrate that the proposed methods enable long-term SLAM in dynamic environments using a large, real-world vision and LIDAR dataset collected over the course of more than a year. This dataset captures a wide variety of dynamics: from short-term scene changes including moving people, cars, changing lighting, and weather conditions; to long-term dynamics including seasonal conditions and structural changes caused by construction.
机译：自主移动机器人技术所需的核心能力之一是使用传感器感知环境的能力。根据这些嘈杂的传感器数据，机器人必须构建环境的表示并将其自身定位在该表示中。此过程称为同时定位和映射（SLAM），是移动机器人技术中几乎所有高级自主行为的先决条件。通过关联机器人在环境中移动时的感官观察，以及通过本体感受传感器观察机器人的自我运动，对机器人的轨迹和环境配置施加了约束。在给定机器人先前的所有感官体验的情况下，这会导致概率优化问题，以找到最可能的机器人轨迹和环境配置。 SLAM已经在以下条件下进行了充分的研究：机器人在相对较短的时间内运行，并且环境在运行过程中基本上是静态的。然而，在对环境中的动态变化进行建模时，长时间执行SLAM仍然是一个挑战。本论文的目的是扩展SLAM的功能，以实现在动态环境中的长期自主运行。本文的贡献主要包括三个方面：首先，我们提出了一个控制SLAM优化问题的计算复杂度的框架，以使其不会随探索时间的增长而无限增长。其次，我们提出了一种学习视觉特征描述符的方法，该方法对于光照的变化更鲁棒，从而可以改善动态环境中的数据关联。最后，我们在SLAM系统中使用了建议的工具，该工具通过将每个位置表示为一组示例视图来捕获位置随时间的变化，从而在地图上显式地模拟了环境的动态。动态环境中的长期SLAM，使用了超过一年的时间收集的大型现实视觉和LIDAR数据集。该数据集捕获了各种各样的动态：短期场景变化，包括移动人员，汽车，变化的照明和天气条件；长期动态，包括季节性条件和施工引起的结构变化。

著录项

  • 作者

    Carlevaris-Bianco, Nicholas D.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Robotics.;Computer science.;Electrical engineering.
  • 学位 Ph.D.
  • 年度 2015
  • 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

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