  Robotic and animal mapping systems share many of the same objectives and
challenges, but differ in one key aspect: where much of the research in robotic
mapping has focused on solving the data association problem, the grid cell
neurons underlying maps in the mammalian brain appear to intentionally break
data association by encoding many locations with a single grid cell neuron. One
potential benefit of this intentional aliasing is both sub-linear map storage
and computational requirements growth with environment size, which we
demonstrated in a previous proof-of-concept study that detected and encoded
mutually complementary co-prime pattern frequencies in the visual map data. In
this research, we solve several of the key theoretical and practical
limitations of that prototype model and achieve significantly better sub-linear
storage growth, a factor reduction in storage requirements per map location,
scalability to large datasets on standard compute equipment and improved
robustness to environments with visually challenging appearance change. These
improvements are achieved through several innovations including a flexible
user-driven choice mechanism for the periodic patterns underlying the new
encoding method, a parallelized chunking technique that splits the map into
sub-sections processed in parallel and a novel feature selection approach that
selects only the image information most relevant to the encoded temporal
patterns. We evaluate our techniques on two large benchmark datasets with the
comparison to the previous state-of-the-art system, as well as providing a
detailed analysis of system performance with respect to parameters such as
required precision performance and the number of cyclic patterns encoded.
