Experience based navigation : theory, practice and implementation
Experience based navigation : theory, practice and implementation
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发表时间：
2012
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通讯作者：
Winson S. Churchill
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作者：
Winson S. Churchill
机器人系统要实现终身自主，必须能够在不断变化的环境中准确导航。在这篇论文中，我们描述，实施和验证一种新的方法来解决长期导航的问题。开始，我们提出了我们的立体视觉里程计系统，它提供了高度准确的姿态估计。我们的方法结合了现有实现中的几种技术和最近发布的图像描述符，简化了解决方案架构。通过对多个数据集的测试，证明了我们的系统的性能和通用性。配备我们的视觉里程计系统，我们描述了一种新的方法来解决终身导航的问题。我们学习一个模型，其复杂性根据场景外观的变化而自然变化。当机器人反复穿越其工作空间时，它会积累不同的视觉体验，这些体验共同隐含地表示场景变化-每个体验都会捕获一种视觉模式。当在以前访问过的区域进行操作时，我们不断尝试定位这些以前的经验，同时运行视觉里程计。未能在足够数量的先前体验中定位指示工作空间的不充分的模型，并且促使将实况图像序列作为新的独特体验来放置。通过这种方式，随着时间的推移，我们可以捕获环境的典型时间变化外观，并且所需的经验数量趋于恒定。虽然我们专注于视觉作为主要传感器，但我们在这里提出的想法同样适用于其他传感器模式。我们展示了我们的方法，在一天中的不同时间，在不同的天气和照明条件下，在三个月的时间内运行的道路车辆上工作。
For robotic systems to realise lifelong autonomy they must be able to navigate accurately in changing environments. In this thesis we describe, implement and validate a new approach to the problem of long-term navigation. To begin, we present our stereo visual odometry system which provides highly accurate pose estimation. Our approach combines several techniques found in existing implementations and a recently published image descriptor that simplifies the solution architecture. The performance and versatility of our system is demonstrated through testing on multiple datasets. Equipped with our visual odometry system, we describe a new approach to the problem of lifelong navigation. We learn a model whose complexity varies naturally in accordance with the variation of scene appearance. As the robot repeatedly traverses its workspace, it accumulates distinct visual experiences that, in concert, implicitly represent the scene variation - each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running the visual odometry. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical temporally varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a three month period at different times of day, in different weather and lighting conditions.