  Learning and recognition is a fundamental process performed in many robot
operations such as mapping and localization. The majority of approaches share
some common characteristics, such as attempting to extract salient features,
landmarks or signatures, and growth in data storage and computational
requirements as the size of the environment increases. In biological systems,
spatial encoding in the brain is definitively known to be performed using a
fixed-size neural encoding framework - the place, head-direction and grid cells
found in the mammalian hippocampus and entorhinal cortex. Particularly
paradoxically, one of the main encoding centers - the grid cells - represents
the world using a highly aliased, repetitive encoding structure where one
neuron represents an unbounded number of places in the world. Inspired by this
system, in this paper we invert the normal approach used in forming mapping and
localization algorithms, by developing a novel place recognition algorithm that
seeks out and leverages repetitive, mutually complementary landmark frequencies
in the world. The combinatorial encoding capacity of multiple different
frequencies enables not only the ability to achieve efficient data storage, but
also the potential for sub-linear storage growth in a learning and recall
system. Using both ground-based and aerial camera datasets, we demonstrate the
system finding and utilizing these frequencies to achieve successful place
recognition, and discuss how this approach might scale to arbitrarily large
global datasets and dimensions.
