We reveal how implementing the homogeneous, multi-scale mapping frameworks observed in the mammalian brain's mapping systems radically improves the performance of a range of current robotic localization techniques. Roboticists have developed a range of predominantly single- or dual-scale heterogeneous mapping approaches (typically locally metric and globally topological) that starkly contrast with neural encoding of space in mammalian brains: a multi-scale map underpinned by spatially responsive cells like the grid cells found in the rodent entorhinal cortex. Yet the full benefits of a homogeneous multi-scale mapping framework remain unknown in both robotics and biology: in robotics because of the focus on single- or two-scale systems and limits in the scalability and open-field nature of current test environments and benchmark datasets; in biology because of technical limitations when recording from rodents during movement over large areas. New global spatial databases with visual information varying over several orders of magnitude in scale enable us to investigate this question for the first time in real-world environments. In particular, we investigate and answer the following questions: why have multi-scale representations, how many scales should there be, what should the size ratio between consecutive scales be and how does the absolute scale size affect performance? We answer these questions by developing and evaluating a homogeneous, multi-scale mapping framework mimicking aspects of the rodent multi-scale map, but using current robotic place recognition techniques at each scale. Results in large-scale real-world environments demonstrate multi-faceted and significant benefits for mapping and localization performance and identify the key factors that determine performance.

中文翻译：

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受生物启发的多尺度融合。

我们揭示了如何实现在哺乳动物大脑的测绘系统中观察到的同质，多尺度的测绘框架从根本上改善一系列当前机器人定位技术的性能。机器人专家已经开发出了一系列主要是单尺度或双尺度的异类映射方法（通常是局部度量和全局拓扑），与哺乳动物大脑中的空间神经编码形成了鲜明的对比：由诸如网格细胞之类的空间响应性细胞所支撑的多尺度地图在啮齿动物的内嗅皮层中发现。然而，同质多尺度映射框架的全部优点在机器人技术和生物学领域仍然未知：在机器人技术中，因为关注于单尺度或两尺度系统以及当前测试环境和基准测试的可扩展性和开放领域的局限性数据集; 由于在大范围移动过程中从啮齿类动物进行记录时的技术局限性而在生物学领域有所发展。新的全球视觉信息量级在几个数量级上变化的全球空间数据库，使我们能够在现实环境中首次调查此问题。特别是，我们调查并回答以下问题：为什么要使用多尺度表示，应该有多少尺度，连续尺度之间的大小比例应该是什么，并且绝对尺度大小将如何影响性能？我们通过开发和评估模仿啮齿类动物多比例尺地图各个方面的同质，多比例尺地图框架来回答这些问题，但是要使用当前在每个比例尺上的机器人位置识别技术。