000265370 001__ 265370
000265370 005__ 20190812204800.0
000265370 037__ $$aCONF
000265370 245__ $$aMobile Robotic Painting of Texture
000265370 260__ $$c2019
000265370 269__ $$a2019
000265370 300__ $$a8 p.
000265370 336__ $$aConference Papers
000265370 500__ $$aPaper MoA1-16.3
000265370 520__ $$aRobotic painting is well-established in controlled factory environments, but there is now potential for mobile robots to do functional painting tasks around the everyday world. An obvious first target for such robots is painting a uniform single color. A step further is the painting of textured images. Texture involves a varying appearance, and requires that paint is delivered accurately onto the physical surface to produce the desired effect. Robotic painting of texture is relevant for architecture and in themed environments. A key challenge for robotic painting of texture is to take a desired image as input, and to generate the paint commands to as closely as possible create the desired appearance, according to the robotic capabilities. This paper describes a deep learning approach to take an input ink map of a desired texture, and infer robotic paint commands to produce that texture. We analyze the trade-offs between quality of reconstructed appearance and ease of execution. Our method is general for different kinds of robotic paint delivery systems, but the emphasis here is on spray painting. More generally, the framework can be viewed as an approach for solving a specific class of inverse imaging problems.
000265370 6531_ $$aComputer Vision for Other Robotic Applications
000265370 6531_ $$aDeep Learning in Robotics and Automation
000265370 700__ $$g266645$$aEl Helou, Majed$$0250358
000265370 700__ $$aMandt, Stephan
000265370 700__ $$aKrause, Andreas
000265370 700__ $$aBeardsley, Paul
000265370 7112_ $$cMontreal, Canada$$aICRA 2019 - IEEE International Conference on Robotics and Automation$$dMay 20-24, 2019
000265370 8564_ $$uhttps://infoscience.epfl.ch/record/265370/files/ICRA_2019_learning_to_paint.pdf$$s3609260
000265370 8560_ $$falessandra.bianchi@epfl.ch
000265370 85641 $$uhttps://ras.papercept.net/conferences/conferences/ICRA19/program/$$yDetailed programme
000265370 909C0 $$pIVRL$$msabine.susstrunk@epfl.ch$$0252320$$zGrolimund, Raphael$$xU10429
000265370 909CO $$pconf$$pIC$$ooai:infoscience.epfl.ch:265370
000265370 960__ $$amajed.elhelou@epfl.ch
000265370 961__ $$aalessandra.bianchi@epfl.ch
000265370 973__ $$aOTHER$$rREVIEWED
000265370 980__ $$aCONF
000265370 981__ $$aoverwrite