For many robotics applications, it is desirable to have relatively low-power and efficient onboard solutions. We took inspiration from insects, such as ants, that are capable of learning and following routes in complex natural environments using relatively constrained sensory and neural systems. Such capabilities are particularly relevant to applications such as agricultural robotics, where visual navigation through dense vegetation remains a challenging task. In this scenario, a route is likely to have high self-similarity and be subject to changing lighting conditions and motion over uneven terrain, and the effects of wind on leaves increase the variability of the input. We used a bioinspired event camera on a terrestrial robot to collect visual sequences along routes in natural outdoor environments and applied a neural algorithm for spatiotemporal memory that is closely based on a known neural circuit in the insect brain. We show that this method is plausible to support route recognition for visual navigation and more robust than SeqSLAM when evaluated on repeated runs on the same route or routes with small lateral offsets. By encoding memory in a spiking neural network running on a neuromorphic computer, our model can evaluate visual familiarity in real time from event camera footage.

中文翻译：

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使用事件相机在穿过植被的路线上进行神经形态序列学习

对于许多机器人应用来说，需要具有相对低功耗和高效的机载解决方案。我们从蚂蚁等昆虫中获得灵感，它们能够使用相对受限的感觉和神经系统在复杂的自然环境中学习和遵循路线。这些功能与农业机器人等应用特别相关，在这些应用中，穿过茂密植被的视觉导航仍然是一项具有挑战性的任务。在这种情况下，路线可能具有较高的自相似性，并且会受到光照条件变化和不平坦地形上运动的影响，并且风对树叶的影响会增加输入的可变性。我们在陆地机器人上使用仿生事件相机来收集自然户外环境中沿路线的视觉序列，并应用一种紧密基于昆虫大脑中已知神经回路的时空记忆神经算法。我们证明，这种方法似乎可以支持视觉导航的路线识别，并且在对同一路线或具有较小横向偏移的路线上的重复运行进行评估时比 SeqSLAM 更稳健。通过在神经形态计算机上运行的尖峰神经网络中对记忆进行编码，我们的模型可以根据事件摄像机镜头实时评估视觉熟悉度。