1. 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

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

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

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

5. Training hand-segmented imagefeatures, in kd-trees 194 191 211 3.2 25.6 4.1... 138 87 53 -1.14 8.6 1.4... filters

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

7. Nearest-neighbors RGB + texture produces better segmentations

8. Segmentation classification + confidencesegmentations

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

10. Application: CoreSLAM images (6 of 25)

11. Application: CoreSLAM scans (6 of 25)

12. Application: CoreSLAM map

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

15. Back-up and extra slides are after this one…

16. Segmentation Accuracy vs. Time tradeoffs

17. 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!

18. CoreSLAM

19. ../TrainingImages/Playspacepswo13Patches/00029/randomBelow/0009.png 194.2575 191.4525 211.4775 195.0 192.0 212.0 8.67070895314 8.76885076564 7.29105573631 211.4775 191.4525 194.2575 212.0 192.0 195.0 7.29105573631 8.76885076564 8.67070895314 3113.18473022 2918.61969259 194.707808243 -8.2951355554e-09 -2.99999999989 3.48871383089 -0.1821058002 1.05861940504 0.798119246226 0.142200402323 1.37643522494 1.51710154734 - 3.21108530262 25.6053670274 4.08979546594 0.20963020652 3.42407524192 7.98287776033 0.655198704147 1.40172476457 1.2862398764 -0.068793854887 7.97682628048 3.32687477389 -0.83219461218 2.58844742173 1.83432937157 -0.239667915222 1.22096756236 3.56803311807 0.0253655120168 0.415881369811 7.11907142389 - 0.829352050768 1.14166676605 4.1432245279 -0.33452013644 -0.0121915477333 6.30267114206 -0.714448599523 0.711384510758 3.38445323448 -0.566103623094 -0.00278409428233 5.92992221434 -0.932328715083 1.17018711051../TrainingImages/Playspacepswo13Patches/00026/randomabove/0007.png 138.1075 87.9525 53.4725 138.0 88.0 53.0 3.65184114523 3.44532200962 5.05512054752 53.4725 87.9525 138.1075 53.0 88.0 138.0 5.05512054752 3.44532200962 3.65184114523 1584.29001984 1485.27385943 99.0473083038 2.67691915514e-09 -2.99999999998 0.839568062542 -0.0913685033632 5.68745353063 0.334375851147 -0.0494906892805 5.40416976808 0.617058294109 -1.14642893693 8.69695694778 1.38147297404 -0.87936321568 4.10330225187 1.59491339286 -0.69926257399 0.679234808869 0.526708837532 -0.253691320988 7.92226252642 0.619482921372 -0.155302887182 1.51598656224 0.455410613623 0.896134364593 4.90251778762 1.44298148203 -1.32247289798 1.54652876227 1.82381743704 1.10446810418 0.850926709651 1.23981013329 -0.346508022218 -0.252958061091 1.53636914298 1.26906228604 1.09496630534 1.45689240135 1.4011501228 1.85701734954 1.68840166501 1.0053643485 0.471348780351 194 191 211 3.2 25.6 4.1 138 87 53 -1.14 8.6 1.4