Autonomous Navigation and Sign Detector Learning

Ellis, L., Pugeault, N. , Öfjäll, K., Hedborg, J., Bowden, R. and Felsberg, M. (2013) Autonomous Navigation and Sign Detector Learning. In: 2013 IEEE Workshop on Robot Vision (WORV), Clearwater Beach, FL, USA, 15-17 Jan 2013, pp. 144-151. ISBN 9781467356480 (doi: 10.1109/WORV.2013.6521929)

Abstract

This paper presents an autonomous robotic system that incorporates novel Computer Vision, Machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous navigation, a mapping is learnt from holistic image features (GIST) onto control parameters using Random Forest regression. Additionally, visual entities (road signs e.g. STOP sign) that are strongly associated to autonomously discovered modes of action (e.g. stopping behaviour) are discovered through a novel Percept-Action Mining methodology. The resulting sign detector is learnt without any supervision (no image labeling or bounding box annotations are used). The complete system is demonstrated on a fully autonomous robotic platform, featuring a single camera mounted on a standard remote control car. The robot carries a PC laptop, that performs all the processing on board and in real-time.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Pugeault, Dr Nicolas
• Authors: Ellis, L., Pugeault, N., Öfjäll, K., Hedborg, J., Bowden, R., and Felsberg, M.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781467356480
• Published Online: 30 May 2013