Difference between revisions of "CISC849 S2018"

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|Background, introduction to the DARPA Robotics Challenge
 
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''Register/add deadline Feb. 19''<br>
 
''Register/add deadline Feb. 19''<br>
 
|PCL tutorial
 
|PCL tutorial
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|Plane/obstacle/object segmentation (3-D)
 
|Plane/obstacle/object segmentation (3-D)
 
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|HW #1 strategies
 
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|Clustering, normal estimation
 
|Clustering, normal estimation
 
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|Registration, features   
 
|Registration, features   
 
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|Object recognition (sample paper)
 
|Object recognition (sample paper)
 
|[http://research.microsoft.com/pubs/145347/bodypartrecognition.pdf "Real-Time Human Pose Recognition in Parts from Single Depth Images"], J. Shotton ''et al.'', CVPR 2011.  ''Recognition, classification, RGB-D''
 
|[http://research.microsoft.com/pubs/145347/bodypartrecognition.pdf "Real-Time Human Pose Recognition in Parts from Single Depth Images"], J. Shotton ''et al.'', CVPR 2011.  ''Recognition, classification, RGB-D''
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|More recognition
 
|More recognition
 
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|Mapping (sample paper)
 
|Mapping (sample paper)
 
|[http://www.ri.cmu.edu/publication_view.html?pub_id=5590 "Real-Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels"], N. Fairfield, G. Kantor, and D. Wettergreen, JFR 2007. ''Occupancy grids, SLAM''
 
|[http://www.ri.cmu.edu/publication_view.html?pub_id=5590 "Real-Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels"], N. Fairfield, G. Kantor, and D. Wettergreen, JFR 2007. ''Occupancy grids, SLAM''
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|Localization
 
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|Particle filters, localization
 
|Particle filters, localization
 
|[http://www2.informatik.uni-freiburg.de/~wurm/papers/hornung10iros.pdf Humanoid Robot Localization in Complex Indoor Environments], A. Hornung, K. Wurm, M. Bennewitz, IROS 2010.  ''Monte Carlo localization''
 
|[http://www2.informatik.uni-freiburg.de/~wurm/papers/hornung10iros.pdf Humanoid Robot Localization in Complex Indoor Environments], A. Hornung, K. Wurm, M. Bennewitz, IROS 2010.  ''Monte Carlo localization''
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|Mapping
 
|Mapping
 
|[http://wiki.ros.org/gmapping gmapping] demos: [http://www.youtube.com/watch?v=7iIDdvCXIFM Pirobot], [http://www.youtube.com/watch?v=_jwBKo0SXng MIT]<br>[http://www.youtube.com/watch?v=F8pdObV_df4 Darmstadt "Hector" mapping]<br>
 
|[http://wiki.ros.org/gmapping gmapping] demos: [http://www.youtube.com/watch?v=7iIDdvCXIFM Pirobot], [http://www.youtube.com/watch?v=_jwBKo0SXng MIT]<br>[http://www.youtube.com/watch?v=F8pdObV_df4 Darmstadt "Hector" mapping]<br>
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|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Spring break''
 
|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Spring break''
 
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|Finish mapping, DARPA Urban Challenge
 
|Finish mapping, DARPA Urban Challenge
 
|[http://www.youtube.com/watch?v=P0NTV2mbJhA Urban Challenge highlights], [http://cs.stanford.edu/group/roadrunner/video.html Stanford clips]
 
|[http://www.youtube.com/watch?v=P0NTV2mbJhA Urban Challenge highlights], [http://cs.stanford.edu/group/roadrunner/video.html Stanford clips]
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|Student paper presentations; project kick-off
 
|Student paper presentations; project kick-off
 
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''Withdraw deadline Apr. 9''
 
''Withdraw deadline Apr. 9''
 
|Student paper presentations
 
|Student paper presentations
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|Perception for stepping
 
|Perception for stepping
 
|"Learning Locomotion over Rough Terrain using Terrain Templates", M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009
 
|"Learning Locomotion over Rough Terrain using Terrain Templates", M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009
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Revision as of 13:42, 30 January 2018

Course information

Title CISC849 Robot Vision and Learning
Shortened URL https://goo.gl/ektJij
Description Survey of image-based 2-D and 3-D sensing algorithms for mobile robot navigation and interaction, including motion estimation, obstacle segmentation, terrain modeling, and object recognition, with a particular focus on deep learning techniques to dramatically improve performance.
When Tuesdays and Thursdays, 11-12:15 pm
Where Smith 102A
Instructor Christopher Rasmussen, 446 Smith Hall, cer@cis.udel.edu
Office hours Tuesdays and Thursdays, 3:30-4:15 pm
Grading
  • 20% Oral paper presentation (individual or pairs, 30 minutes)
  • 30% Two programming assignments (individual)
  • 50% Final project (teams of 1-3)
    • 10% = 2 page proposal, including planned methods, citations of relevant papers, data sources, and division of labor
    • 10% = Joint 15-minute presentation on final results, with accompanying slides
    • 30% = Actual results and estimated effort, factoring in difficulty of problem tackled
Academic policies Programming projects are due by midnight of the deadline day (with a grace period of a few hours afterward...after sunrise is definitely late). A late homework is a 0 without a valid prior excuse. To give you a little flexibility, you have 6 "late days" to use on homeworks to extend the deadline by one day each without penalty. No more than three late days may be used per assignment. Late days will automatically be subtracted, but as a courtesy please notify the instructor in an e-mail of your intention to use late days before the deadline. See submission instructions below.

Students can discuss problems with one another in general terms, but must work independently on programming assignments. This also applies to online and printed resources: you may consult them as references (as long as you cite them), but the words and source code you turn in must be yours alone. The University's policies on academic dishonesty are set forth in the student code of conduct here.

Homeworks Assignment submissions should consist of a directory containing all code (your .cpp files, makefile, etc.), any output data generated (e.g., images, movies, etc.), and an explanation of your approach, what worked and didn't work, etc. contained in a separate text or HTML file. Do not submit executables or .o files, please! The directory you submit for each assignment should be packaged by tar'ing and gzip'ing it or just zip'ing it. The resulting file should be submitted through Canvas.

You may develop your C/C++ code in any fashion that is convenient--that is, with any compiler and operating system that you want. However, we will be grading your homework on a Linux system with a makefile, and so you must avoid OS- and hardware-specific functions and provide a makefile for us that will work (like one of the templates above).

Possible Papers to Present (not a complete list)

  • "Collaborative mapping of an earthquake-damaged building via ground and aerial robots", N. Michael et al., JFR 2012. UAV, UGV, disaster, mapping
  • "Vision Based Victim Detection from Unmanned Aerial Vehicles", M. Andriluka et al., IROS 2010. UAV, person detection
  • "Biped Navigation in Rough Environments using On-board Sensing", J. Chestnutt, Y. Takaoka, K. Suga, K. Nishiwaki, J. Kuffner, and S. Kagami, IROS 2009. Footstep planning, ladar, plane fitting
  • "Real-Time Navigation in 3D Environments Based on Depth Camera Data", D. Maier, A. Hornung, and M. Bennewitz, Humanoids 2012. RGB-D, localization, mapping, planning
  • "Robotic Grasping of Novel Objects using Vision", A. Saxena, J. Driemeyer, A. Ng, IJRR 2008. Grasping, learning
  • "Self-supervised Monocular Road Detection in Desert Terrain", H. Dahlkamp, A. Kaehler, D. Stavens, S. Thrun, and G. Bradski, 2006. DARPA GC, color similarity, segmentation
  • "Multi-Sensor Lane Finding in Urban Road Networks", A. Huang, D. Moore, M. Antone, E. Olson, S. Teller, RSS 2008. DARPA UC, edge detection, robust curve fitting, tracking
  • "High fidelity day/night stereo mapping with vegetation and negative obstacle detection for vision-in-the-loop walking", M. Bajracharya et al., IROS 2013. LS3, dense stereo depth, visual odometry
  • (taken) "Autonomous Door Opening and Plugging In with a Personal Robot", W. Meeussen et al., IROS 2010. PR2, grasping

Instructions for Homeworks

Operating system
  • If you don't have a Linux distribution running currently, it's not hard to add one to your machine. The Ubuntu website has comprehensive instructions on installing it from different sources (CD-ROM, USB stick, etc.) on a separate partition (aka "dual booting"). I recommend version 16.04.3 LTS.
  • You will get an account on my lab workstation for the duration of the course.
Software
  • ROS
    • Installation instructions (Kinetic, Ubuntu 16.04, Desktop-Full Install). This will take about 430 Mb of space, and it includes PCL for 3-D point cloud processing and OpenCV for computer vision/image processing. We will mainly be using ROS for these included libraries and the visualization functionality of the rviz tool, so don't worry about "learning" ROS. If you're curious, links to more information are below.
    • "Cheatsheet"
    • Tutorial videos
    • Rviz user guide
  • PCL
    • Do not install separately--we will just use version (1.7) included in ROS Kinetic
  • OpenCV
    • Do not install separately--we will just use version (3) included in ROS Kinetic

Schedule

Note: The blue squares in the "#" column below indicate Tuesdays.

# Date Topic Links/Readings/videos Assignments/slides
1 Feb. 6 Background, introduction to the DARPA Robotics Challenge slides
2 Feb. 8
3 Feb. 13 DRC algorithm components slides
4 Feb. 15

Register/add deadline Feb. 19

 PCL tutorial slides

Sample PCL programs

5 Feb. 20 Plane/obstacle/object segmentation (3-D) HW #1
6 Feb. 22 HW #1 strategies

(Clustering example added here)

7 Feb. 27 Clustering, normal estimation slides

(Normals exampled added here)

8 Mar. 1 Registration, features PCL registration, features
HW #1 due
9 Mar. 6 Object recognition (sample paper) "Real-Time Human Pose Recognition in Parts from Single Depth Images", J. Shotton et al., CVPR 2011. Recognition, classification, RGB-D slides
10 Mar. 8 More recognition HW #2
11 Mar. 13 Mapping (sample paper) "Real-Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels", N. Fairfield, G. Kantor, and D. Wettergreen, JFR 2007. Occupancy grids, SLAM slides
12 Mar. 15 Localization ETH localization lectures: 1 2
13 Mar. 20 Particle filters, localization Humanoid Robot Localization in Complex Indoor Environments, A. Hornung, K. Wurm, M. Bennewitz, IROS 2010. Monte Carlo localization Thrun particle filtering slides
HW #2 due
14 Mar. 22 Mapping gmapping demos: Pirobot, MIT
Darmstadt "Hector" mapping

Nao 3-D mapping and planning: 1, 2

Thrun FastSLAM slides (grids from slide 29)
Accompanying Stachniss lecture
Paper presentation choice due Friday, March 28
Mar. 27 NO CLASS
Spring break
Mar. 29 NO CLASS
Spring break
15 Apr. 3 Finish mapping, DARPA Urban Challenge Urban Challenge highlights, Stanford clips Project proposal due Monday, April 7
16 Apr. 5 Student paper presentations; project kick-off
Apr. 10
17 Apr. 12 Student paper presentations
18 Apr. 17

Withdraw deadline Apr. 9

Student paper presentations
19 Apr. 19 Mid-project review; student paper presentation
20 Apr. 24 Student paper presentations
21 Apr. 26 Motion planning background slides
22 May 1 Perception for stepping "Learning Locomotion over Rough Terrain using Terrain Templates", M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009
23 May 3 Final project review
24 May 8 "Bonus" material
25 May 10 Final project presentations Final project due
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