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Active perception and modeling of deformable surfaces using Gaussian processes and position-based dynamics
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. 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), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-5750-9655
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2016 (English)In: IEEE-RAS International Conference on Humanoid Robots, IEEE, 2016, p. 530-537Conference paper, Published paper (Refereed)
Abstract [en]

Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive force-based simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an Optoforce sensor on different deformable surfaces.

Place, publisher, year, edition, pages
IEEE, 2016. p. 530-537
Keywords [en]
Active perception, Deformability modeling, Gaussian process, Position-based dynamics, Tactile exploration, Anthropomorphic robots, Deformation, Dynamics, Gaussian noise (electronic), Probability distributions, Robots, Active perceptions, Environmental observation, Gaussian process regression, Gaussian Processes, Multiple interactions, Physical interactions, Probabilistic framework, Gaussian distribution
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-202842DOI: 10.1109/HUMANOIDS.2016.7803326ISI: 000403009300081Scopus ID: 2-s2.0-85010190205ISBN: 9781509047185 (print)OAI: oai:DiVA.org:kth-202842DiVA, id: diva2:1082624
Conference
16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, 15 November 2016 through 17 November 2016
Note

QC 20170317

Available from: 2017-03-17 Created: 2017-03-17 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
Keywords
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, SergioGüler, PürenKjellström, HedvigKragic, Danica

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