Autonomous Navigation and Sign Detector Learning
2013 (English)In: IEEE Workshop on Robot Vision(WORV) 2013, IEEE , 2013, 144-151 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2013. 144-151 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-86214DOI: 10.1109/WORV.2013.6521929ISBN: 978-1-4673-5647-3 (online)ISBN: 978-1-4673-5646-6 (print)OAI: oai:DiVA.org:liu-86214DiVA: diva2:575736
IEEE Workshop on Robot Vision (WORV 2013), 15-17 January 2013, Clearwater Beach, FL, USA
ProjectsELLIITETTCUASUK EPSRC: EP/H023135/1