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

Understand

Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates".

• These attributes make them, at least in theory, particularly suitable for enabling tasks like navigation and mapping on high speed robotic platforms under challenging lighting conditions, a task which has been particularly challenging for traditional algorithms and camera sensors.
• Before these tasks become feasible however, progress must be made towards adapting and innovating current RGB-camera-based algorithms to work with event-based cameras.
• In this paper we present ongoing research investigating two distinct approaches to incorporating event-based cameras for robotic navigation: the investigation of suitable place recognition / loop closure techniques, and the development of efficient neural implementations of place recognition techniques that enable the possibility of place recognition using event-based cameras at very high frame rates using neuromorphic computing hardware.