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Autonomous Navigation and Sign Detector Learning
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
CVSSP, University of Surrey, Guildford, UK.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
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2013 (Engelska)Ingår i: IEEE Workshop on Robot Vision(WORV) 2013, IEEE , 2013, s. 144-151Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE , 2013. s. 144-151
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:liu:diva-86214DOI: 10.1109/WORV.2013.6521929ISBN: 978-1-4673-5647-3 (tryckt)ISBN: 978-1-4673-5646-6 (tryckt)OAI: oai:DiVA.org:liu-86214DiVA, id: diva2:575736
Konferens
IEEE Workshop on Robot Vision (WORV 2013), 15-17 January 2013, Clearwater Beach, FL, USA
Projekt
ELLIITETTCUASUK EPSRC: EP/H023135/1Tillgänglig från: 2012-12-11 Skapad: 2012-12-11 Senast uppdaterad: 2016-06-14
Ingår i avhandling
1. Online Learning for Robot Vision
Öppna denna publikation i ny flik eller fönster >>Online Learning for Robot Vision
2014 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2014. s. 62
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1678
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-110892 (URN)10.3384/lic.diva-110892 (DOI)978-91-7519-228-4 (ISBN)
Presentation
2014-10-24, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (Svenska)
Opponent
Handledare
Forskningsfinansiär
EU, FP7, Sjunde ramprogrammet, 247947Vetenskapsrådet
Tillgänglig från: 2014-09-26 Skapad: 2014-09-26 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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Ellis, LiamÖfjäll, KristofferHedborg, JohanFelsberg, Michael

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