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

TL;DR

This paper explores adapting place recognition algorithms for event-based cameras, focusing on neural implementations that leverage neuromorphic hardware for high-speed robotic navigation in challenging lighting conditions.

Contribution

It introduces novel approaches to place recognition with event-based cameras, including neural implementations optimized for neuromorphic hardware, advancing high-speed robotic navigation.

Findings

Preliminary results show promise for event-based place recognition.

Neural implementations enable high frame rate processing.

Potential for improved navigation in challenging environments.

Abstract

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.