Towards Adapting Autonomous Vehicle Technology for the Improvement of Personal Mobility Devices

Abstract

Personal Mobility Devices (PMDs) incorporated with autonomy, have great potential in becoming an essential building block of smart transportation infrastructures of the future. However, autonomous vehicle technologies currently employ large and expensive sensors / computers and resource intensive algorithms, which are not suitable for low cost, small form factor PMDs. In this paper, a mobility scooter is retrofitted with a low cost sensing and computing package with the aim of achieving autonomous driving capability. As a first step, a novel, real time, low cost and resource efficient vision only localisation framework based on Convolutional Neural Network (CNN) oriented feature extraction and extended Kalman filter oriented state estimation is presented. Real world experiments in a suburban environment are presented to demonstrate the effectiveness of the proposed localisation framework.