title: Bio-inspired homogeneous multi-scale place recognition.
abstract: Robotic mapping and localization systems typically operate at either one fixed spatial scale, or over two, combining a local metric map and a global topological map. In contrast, recent high profile discoveries in neuroscience have indicated that animals such as rodents navigate the world using multiple parallel maps, with each map encoding the world at a specific spatial scale. While a number of theoretical-only investigations have hypothesized several possible benefits of such a multi-scale mapping system, no one has comprehensively investigated the potential mapping and place recognition performance benefits for navigating robots in large real world environments, especially using more than two homogeneous map scales. In this paper we present a biologically-inspired multi-scale mapping system mimicking the rodent multi-scale map. Unlike hybrid metric-topological multi-scale robot mapping systems, this new system is homogeneous, distinguishable only by scale, like rodent neural maps. We present methods for training each network to learn and recognize places at a specific spatial scale, and techniques for combining the output from each of these parallel networks. This approach differs from traditional probabilistic robotic methods, where place recognition spatial specificity is passively driven by models of sensor uncertainty. Instead we intentionally create parallel learning systems that learn associations between sensory input and the environment at different spatial scales. We also conduct a systematic series of experiments and parameter studies that determine the effect on performance of using different neural map scaling ratios and different numbers of discrete map scales. The results demonstrate that a multi-scale approach universally improves place recognition performance and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms. We analyze the results and discuss the implications with respect to several recent discoveries and theories regarding how multi-scale neural maps are learnt and used in the mammalian brain.
authors.affiliation: School of Electrical Engineering and Computer Science, Queensland University of Technology, Australia; Australian Centre for Robotic Vision, Queensland University of Technology, Australia. Electronic address: zetao.chen@hdr.qut.edu.au.
authors.forename: Zetao
authors.initials: Z
authors.lastname: Chen
authors.affiliation: School of Electrical Engineering and Computer Science, Queensland University of Technology, Australia.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Lowry
authors.affiliation: School of Electrical Engineering and Computer Science, Queensland University of Technology, Australia; Australian Centre for Robotic Vision, Queensland University of Technology, Australia.
authors.forename: Adam
authors.initials: A
authors.lastname: Jacobson
authors.affiliation: Center for Memory and Brain and Graduate Program for Neuroscience, Boston University, United States.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Hasselmo
authors.affiliation: School of Electrical Engineering and Computer Science, Queensland University of Technology, Australia; Australian Centre for Robotic Vision, Queensland University of Technology, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Milford
mesh_terms: D000465:Algorithms; D032701:Biomimetics; D007858:Learning; D016571:Neural Networks, Computer; D021641:Recognition, Psychology; D012371:Robotics / Q000379:methods*
keywords: Bio-inspired; Metric learning; Multi-scale place recognition; Robot localization