Change search
ReferencesLink to record
Permanent link

Direct link
Autonomous Navigation and Sign Detector Learning
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
CVSSP, University of Surrey, Guildford, UK.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Show others and affiliations
2013 (English)In: IEEE Workshop on Robot Vision(WORV) 2013, IEEE , 2013, 144-151 p.Conference paper (Refereed)
Abstract [en]

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.
National Category
Engineering and Technology
URN: 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: diva2:575736
IEEE Workshop on Robot Vision (WORV 2013), 15-17 January 2013, Clearwater Beach, FL, USA
Available from: 2012-12-11 Created: 2012-12-11 Last updated: 2016-06-14
In thesis
1. Online Learning for Robot Vision
Open this publication in new window or tab >>Online Learning for Robot Vision
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In tele-operated robotics applications, the primary information channel from the robot to its human operator is a video stream. For autonomous robotic systems however, a much larger selection of sensors is employed, although the most relevant information for the operation of the robot is still available in a single video stream. The issue lies in autonomously interpreting the visual data and extracting the relevant information, something humans and animals perform strikingly well. On the other hand, humans have great diculty expressing what they are actually looking for on a low level, suitable for direct implementation on a machine. For instance objects tend to be already detected when the visual information reaches the conscious mind, with almost no clues remaining regarding how the object was identied in the rst place. This became apparent already when Seymour Papert gathered a group of summer workers to solve the computer vision problem 48 years ago [35].

Articial learning systems can overcome this gap between the level of human visual reasoning and low-level machine vision processing. If a human teacher can provide examples of what to be extracted and if the learning system is able to extract the gist of these examples, the gap is bridged. There are however some special demands on a learning system for it to perform successfully in a visual context. First, low level visual input is often of high dimensionality such that the learning system needs to handle large inputs. Second, visual information is often ambiguous such that the learning system needs to be able to handle multi modal outputs, i.e. multiple hypotheses. Typically, the relations to be learned  are non-linear and there is an advantage if data can be processed at video rate, even after presenting many examples to the learning system. In general, there seems to be a lack of such methods.

This thesis presents systems for learning perception-action mappings for robotic systems with visual input. A range of problems are discussed, such as vision based autonomous driving, inverse kinematics of a robotic manipulator and controlling a dynamical system. Operational systems demonstrating solutions to these problems are presented. Two dierent approaches for providing training data are explored, learning from demonstration (supervised learning) and explorative learning (self-supervised learning). A novel learning method fullling the stated demands is presented. The method, qHebb, is based on associative Hebbian learning on data in channel representation. Properties of the method are demonstrated on a vision-based autonomously driving vehicle, where the system learns to directly map low-level image features to control signals. After an initial training period, the system seamlessly continues autonomously. In a quantitative evaluation, the proposed online learning method performed comparably with state of the art batch learning methods.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 62 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1678
National Category
Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:liu:diva-110892 (URN)10.3384/lic.diva-110892 (DOI)978-91-7519-228-4 (print) (ISBN)
2014-10-24, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (Swedish)
EU, FP7, Seventh Framework Programme, 247947Swedish Research Council
Available from: 2014-09-26 Created: 2014-09-26 Last updated: 2016-05-04Bibliographically approved

Open Access in DiVA

fulltext(10505 kB)718 downloads
File information
File name FULLTEXT01.pdfFile size 10505 kBChecksum SHA-512
Type fulltextMimetype application/pdf
Supplemental Material (Video)(65169 kB)10 downloads
File information
File name MOVIE01.mpegFile size 65169 kBChecksum SHA-512
Type movieMimetype video/mpeg

Other links

Publisher's full text

Search in DiVA

By author/editor
Ellis, LiamÖfjäll, KristofferHedborg, JohanFelsberg, Michael
By organisation
Computer VisionThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 718 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 516 hits
ReferencesLink to record
Permanent link

Direct link