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Biologically plausible visual homing methods based on optical flow techniques

机译：基于光流技术的生物学上合理的视觉归位方法

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Insects are able to return to important places in their environment by storing an image of the surroundings while at the goal, and later computing a home direction from a matching between this 'snapshot' image and the currently perceived image. Very similar ideas are pursued for the visual navigation of mobile robots. A wide range of different solutions for the matching between the two images have been suggested. This paper explores the application of optical flow techniques for visual homing. The performance of five different flow techniques and a reference method is analysed based on image collections from three different indoor environments. We show that block matching, two simple variants of block matching and two even simpler differential techniques produce robust homing behaviour, despite the simplicity of the matched features. Our analysis reveals that visual homing can succeed even in the presence of many incorrect feature correspondences, and that low-frequency features are sufficient for homing. In particular, the successful application of differential methods opens new vistas on the visual homing problem, both as plausible and parsimonious models of visual insect navigation, and as a starting point for novel robot navigation methods.
机译：通过在目标对准时存储周围环境的图像，然后根据此“快照”图像与当前感知到的图像之间的匹配度来计算返航方向，昆虫可以返回到其环境中的重要场所。对于移动机器人的视觉导航，追求非常相似的想法。对于两个图像之间的匹配，已经提出了各种各样的不同解决方案。本文探讨了光流技术在视觉归位中的应用。基于来自三个不同室内环境的图像收集，分析了五种不同的流动技术和一种参考方法的性能。我们展示了块匹配，块匹配的两个简单变体以及两种甚至更简单的差分技术，尽管匹配的功能很简单，但它们仍具有强大的归位行为。我们的分析表明，即使存在许多不正确的特征对应关系，视觉归位也可以成功，低频特征足以实现归位。特别是，差分方法的成功应用为视觉归巢问题开辟了新的视野，既是视觉昆虫导航的合理模型，又是简约的模型，并且是新型机器人导航方法的起点。

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