3 research outputs found
Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost
Robotic and animal mapping systems share many challenges and characteristics:
they must function in a wide variety of environmental conditions, enable the
robot or animal to navigate effectively to find food or shelter, and be
computationally tractable from both a speed and storage perspective. With
regards to map storage, the mammalian brain appears to take a diametrically
opposed approach to all current robotic mapping systems. Where robotic mapping
systems attempt to solve the data association problem to minimise
representational aliasing, neurons in the brain intentionally break data
association by encoding large (potentially unlimited) numbers of places with a
single neuron. In this paper, we propose a novel method based on supervised
learning techniques that seeks out regularly repeating visual patterns in the
environment with mutually complementary co-prime frequencies, and an encoding
scheme that enables storage requirements to grow sub-linearly with the size of
the environment being mapped. To improve robustness in challenging real-world
environments while maintaining storage growth sub-linearity, we incorporate
both multi-exemplar learning and data augmentation techniques. Using large
benchmark robotic mapping datasets, we demonstrate the combined system
achieving high-performance place recognition with sub-linear storage
requirements, and characterize the performance-storage growth trade-off curve.
The work serves as the first robotic mapping system with sub-linear storage
scaling properties, as well as the first large-scale demonstration in
real-world environments of one of the proposed memory benefits of these
neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and
Automation Letter