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Event-based cameras offer much potential to the fields of robotics andcomputer vision, in part due to their large dynamic range and extremely high"frame rates". These attributes make them, at least in theory, particularlysuitable for enabling tasks like navigation and mapping on high speed roboticplatforms under challenging lighting conditions, a task which has beenparticularly challenging for traditional algorithms and camera sensors. Beforethese tasks become feasible however, progress must be made towards adapting andinnovating current RGB-camera-based algorithms to work with event-basedcameras. In this paper we present ongoing research investigating two distinctapproaches to incorporating event-based cameras for robotic navigation: theinvestigation of suitable place recognition / loop closure techniques, and thedevelopment of efficient neural implementations of place recognition techniquesthat enable the possibility of place recognition using event-based cameras atvery high frame rates using neuromorphic computing hardware.

Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM