A Sequence-Based Neuronal Model for Mobile Robot Localization

Abstract

Inferring ego position by recognizing previously seen places in the world is an essential capability for autonomous mobile systems. Recent advances have addressed increasingly challenging recognition problems, e.g. long-term vision-based localization despite severe appearance changes induced by changing illumination, weather or season. Since robots typically move continuously through an environment, there is high correlation within consecutive sensory inputs and across similar trajectories. Exploiting this sequential information is a key element of some of the most successful approaches for place recognition in changing environments. We present a novel, neurally inspired approach that uses sequences for mobile robot localization. It builds upon Hierarchical Temporal Memory (HTM), an established neuroscientific model of working principles of the human neocortex. HTM features two properties that are interesting for place recognition applications: (1) It relies on sparse distributed representations, which are known to have high representational capacity and high robustness towards noise. (2) It heavily exploits the sequential structure of incoming sensory data. In this paper, we discuss the importance of sequence information for mobile robot localization, we provide an introduction to HTM, and discuss theoretical analogies between the problem of place recognition and HTM. We then present a novel approach, applying a modified version of HTM’s higher order sequence memory to mobile robot localization. Finally we demonstrate the capabilities of the proposed approach on a set of simulation-based experiments.