Utilizing the Structure of Field Lines for Efficient Soccer Robot Localization
Utilizing the Structure of Field Lines for Efficient Soccer Robot Localization
复制标题
利用场线结构进行高效的足球机器人定位
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Sven Behnke
中科院分区：
文献类型：
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作者：
Hannes Schulz;Sven Behnke
摘要 球场上的自我定位是足球机器人（例如足球机器人）的关键感知任务之一。 RoboCup比赛中，必须解决。这个问题变得更加困难，因为 RoboCup 的规则越来越不鼓励在赛场上仅以颜色为导向。当场地面积增大时，场地边界标记和球门会变得更小且色彩更少。因此，为了进行稳健的游戏，机器人需要保持概率姿势估计并依赖更微妙的环境线索。场线是特别有趣的特征，因为它们几乎不会完全被遮挡，观察它们会显着减少场上可能姿势的数量。在这项工作中，我们提出了一种在足球场上基于线的自定位方法。与以前的工作不同，我们的方法首先从图像中恢复线结构图。从图中，我们可以轻松导出线条和角点等特征。最后，我们描述了在粒子滤波器中有效使用派生特征的优化。本文中描述的方法经常用于我们的人形足球机器人，该机器人赢得了 2009-2011 年 RoboCup TeenSize 比赛。
Abstract Self-localization on the field is one of the key perceptual tasks that a soccer robot, e.g. in the RoboCup competitions, must solve. This problem becomes harder, as the rules in RoboCup more and more discourage a solely color-based orientation on the field. While the field size increases, field boundary markers and goals become smaller and less colorful. For robust game play, robots, therefore, need to maintain a probabilistic pose estimate and rely on more subtle environmental clues. Field lines are particularly interesting features, because they are hardly ever completely occluded and observing them significantly reduces the number of possible poses on the field. In this work, we present a method for line-based self-localization on a soccer field. Unlike previous work, our method first recovers a line structure graph from the image. From the graph, we can then easily derive features such as lines and corners. Finally, we describe optimizations for efficient use of the derived features in a particle filter. The method described in this article is used regularly on our humanoid soccer robots, which won the RoboCup TeenSize competitions in the years 2009–2011.