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Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-6716-1111
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2016 (English)In: 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 582-589Conference paper, Published paper (Refereed)
Abstract [en]

In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an online probabilistic framework that merges visual and tactile measurements using Gaussian Random Field and Gaussian Process Implicit Surfaces. The system investigates incomplete point clouds in order to find a small set of regions of interest which are then physically explored with a robotic arm equipped with tactile sensors. We show experimental results obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce sensors on different scenarios. We then demostrate how to use the online framework for object detection and terrain classification.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 582-589
Keyword [en]
Active perception, Surface reconstruction, Gaussian process, Implicit surface, Random field, Tactile exploration
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-202672DOI: 10.1109/IROS.2016.7759112ISI: 000391921700086Scopus ID: 2-s2.0-85006371409ISBN: 978-1-5090-3762-9 (print)OAI: oai:DiVA.org:kth-202672DiVA, id: diva2:1078703
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OCT 09-14, 2016, Daejeon, SOUTH KOREA
Note

QC 20170306

Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2018-04-11Bibliographically approved
In thesis
1. Enhancing geometric maps through environmental interactions
Open this publication in new window or tab >>Enhancing geometric maps through environmental interactions
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The deployment of rescue robots in real operations is becoming increasingly commonthanks to recent advances in AI technologies and high performance hardware. Rescue robots can now operate for extended period of time, cover wider areas andprocess larger amounts of sensory information making them considerably more usefulduring real life threatening situations, including both natural or man-made disasters.

In this thesis we present results of our research which focuses on investigating ways of enhancing visual perception for Unmanned Ground Vehicles (UGVs) through environmental interactions using different sensory systems, such as tactile sensors and wireless receivers.

We argue that a geometric representation of the robot surroundings built upon vision data only, may not suffice in overcoming challenging scenarios, and show that robot interactions with the environment can provide a rich layer of new information that needs to be suitably represented and merged into the cognitive world model. Visual perception for mobile ground vehicles is one of the fundamental problems in rescue robotics. Phenomena such as rain, fog, darkness, dust, smoke and fire heavily influence the performance of visual sensors, and often result in highly noisy data, leading to unreliable or incomplete maps.

We address this problem through a collection of studies and structure the thesis as follow:Firstly, we give an overview of the Search & Rescue (SAR) robotics field, and discuss scenarios, hardware and related scientific questions.Secondly, we focus on the problems of control and communication. Mobile robotsrequire stable communication with the base station to exchange valuable information. Communication loss often presents a significant mission risk and disconnected robotsare either abandoned, or autonomously try to back-trace their way to the base station. We show how non-visual environmental properties (e.g. the WiFi signal distribution) can be efficiently modeled using probabilistic active perception frameworks based on Gaussian Processes, and merged into geometric maps so to facilitate the SAR mission. We then show how to use tactile perception to enhance mapping. Implicit environmental properties such as the terrain deformability, are analyzed through strategic glancesand touches and then mapped into probabilistic models.Lastly, we address the problem of reconstructing objects in the environment. Wepresent a technique for simultaneous 3D reconstruction of static regions and rigidly moving objects in a scene that enables on-the-fly model generation. Although this thesis focuses mostly on rescue UGVs, the concepts presented canbe applied to other mobile platforms that operates under similar circumstances. To make sure that the suggested methods work, we have put efforts into design of user interfaces and the evaluation of those in user studies.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 58
Series
TRITA-EECS-AVL ; 2018:26
Keyword
Gaussian Processes Robotics UGV Active perception geometric maps
National Category
Engineering and Technology
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-225957 (URN)978-91-7729-720-8 (ISBN)
Public defence
2018-04-18, F3, Lindstedtsvägen 26, Sing-Sing, floor 2, KTH Campus, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme
Note

QC 20180411

Available from: 2018-04-11 Created: 2018-04-11 Last updated: 2018-04-11Bibliographically approved

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Caccamo, SergioKragic, Danica

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