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Conference Paper: Optimization-based automatic parameter tuning for stereo vision

TitleOptimization-based automatic parameter tuning for stereo vision
Authors
KeywordsProcess Automation
Issue Date2015
PublisherIEEE. The Conference program's website is located at https://ras.papercept.net/conferences/conferences/CASE15/program/
Citation
The 11th Annual IEEE International Conference on Automation Science and Engineering (IEEE CASE 2015), Gothenburg, Sweden, 24-28 August 2015. How to Cite?
AbstractStereo vision is an important 3D sensing technique for producing dense point clouds required for robotic navigation and manipulation. It can provide excellent depth resolution at high frame rates and is potentially smaller, cheaper and consumes less power than systems using active sensor devices, due to its use of standard imaging components like cameras. However, stereo vision system can generate high quality point clouds only when its parameters are appropriately tuned. To tune these parameters manually is not only tedious but also challenging, due to the large number of parameters and their non-linear effect on the depth map quality. In this paper, we present an optimization-based method to automatically tune the stereo parameters. In particular, we first adjust the disparity range to ensure the entire scene can be covered in the resultant depth map, and then use non-linear optimization to refine other parameters for the optimal depth map quality. Our tuning process is efficient and can update adaptively according to changing environment. Experiments on the teleoperation tasks using the Atlas robot validate our approach, and demonstrate the improvement it brings for the teleoperation effectiveness. © IEEE Robotics & Automation Society.
DescriptionPaper We_2-T5.4
Persistent Identifierhttp://hdl.handle.net/10722/211509

 

DC FieldValueLanguage
dc.contributor.authorCheung, ECH-
dc.contributor.authorChan, JQ-
dc.contributor.authorWong, JL-
dc.contributor.authorPan, J-
dc.date.accessioned2015-07-16T02:09:37Z-
dc.date.available2015-07-16T02:09:37Z-
dc.date.issued2015-
dc.identifier.citationThe 11th Annual IEEE International Conference on Automation Science and Engineering (IEEE CASE 2015), Gothenburg, Sweden, 24-28 August 2015.-
dc.identifier.urihttp://hdl.handle.net/10722/211509-
dc.descriptionPaper We_2-T5.4-
dc.description.abstractStereo vision is an important 3D sensing technique for producing dense point clouds required for robotic navigation and manipulation. It can provide excellent depth resolution at high frame rates and is potentially smaller, cheaper and consumes less power than systems using active sensor devices, due to its use of standard imaging components like cameras. However, stereo vision system can generate high quality point clouds only when its parameters are appropriately tuned. To tune these parameters manually is not only tedious but also challenging, due to the large number of parameters and their non-linear effect on the depth map quality. In this paper, we present an optimization-based method to automatically tune the stereo parameters. In particular, we first adjust the disparity range to ensure the entire scene can be covered in the resultant depth map, and then use non-linear optimization to refine other parameters for the optimal depth map quality. Our tuning process is efficient and can update adaptively according to changing environment. Experiments on the teleoperation tasks using the Atlas robot validate our approach, and demonstrate the improvement it brings for the teleoperation effectiveness. © IEEE Robotics & Automation Society.-
dc.languageeng-
dc.publisherIEEE. The Conference program's website is located at https://ras.papercept.net/conferences/conferences/CASE15/program/-
dc.relation.ispartofIEEE International Conference on Automation Science and Enginnering, IEEE CASE 2015-
dc.rightsIEEE International Conference on Automation Science and Enginnering, IEEE CASE 2015. Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectProcess Automation-
dc.titleOptimization-based automatic parameter tuning for stereo vision-
dc.typeConference_Paper-
dc.identifier.emailCheung, ECH: ernest1@hku.hk-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.identifier.hkuros244991-
dc.publisher.placeUnited States-

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