Robotics Education Track 2:45 Preview of the PIRE challenge Paul Oh and Doug Blank 3:15 PixelLaser M Korbel, M Leece, K Lei, N Lesperance, S Matsumoto,

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Presentation transcript:

Robotics Education Track 2:45 Preview of the PIRE challenge Paul Oh and Doug Blank 3:15 PixelLaser M Korbel, M Leece, K Lei, N Lesperance, S Matsumoto, and Z Dodds 3:25 Calliope Owen Watson and Dave Touretzky 3:35 Shake Time! Marynel Vazquez, Alex May, Wei-Hsuan Chen 3:45 An iRobot Create Simulator Andrew Hettlinger and Matthew R. Boutell 3:55 IMP: The Intelligent Mobile Projector Keith O'Hara, Anis Zaman, and Aaron Strauss 4:05 Developing a Framework for Team-based Robotics Research Elizabeth Sklar, Simon Parsons, and Susan Epstein 4:15 An Intensive Introductory Robotics Course Without Prerequisites Julian Mason, Gavin Taylor 4:25 PREOP Monica Anderson 3:00-3:15 Break

PixelLaser: Range scans from image segmentation Nicole Lesperance ’10 Michael Leece ’10 Steve Matsumoto ’11 Max Korbel ’12 Kenny Lei ’14 Zach Dodds REU

Inspiration Horswill (polly) ’94 Saxena (rccar) ’05

Scans ? C. Plagemann et al., ICRA 2008 platform"omnicam" imageserrors...

Training hand-segmented imagefeatures, in kd-trees filters

Nearest-neighbor classification RGB alone doesn’t work well…

Nearest-neighbors RGB + texture produces better segmentations

Segmentation classification + confidencesegmentations

From segmentation to scan pixel row number range row-to-range map scan segmentation

Application: CoreSLAM images (6 of 25)

Application: CoreSLAM scans (6 of 25)

Application: CoreSLAM map

Application: MCL Still a work in progress... (speed!)

Back-up and extra slides are after this one…

Segmentation Accuracy vs. Time tradeoffs

Scans ΔyΔy yoyo θ α θ α Δy cos(θ) f f / cos(θ) Δy sin(θ) D h For a fixed horizon: For non-fixed horizon: Wheeeee!

CoreSLAM

../TrainingImages/Playspacepswo13Patches/00029/randomBelow/0009.png e /TrainingImages/Playspacepswo13Patches/00026/randomabove/0007.png e