Difference between revisions of "CISC849 S2018"
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==Possible Papers to Present (not a complete list)== | ==Possible Papers to Present (not a complete list)== | ||
| + | * [https://arxiv.org/pdf/1604.07316.pdf End to End Learning for Self-Driving cars], Bojarski ''et al.'', 2016 | ||
* "Collaborative mapping of an earthquake-damaged building via ground and aerial robots", N. Michael ''et al.'', JFR 2012. ''UAV, UGV, disaster, mapping'' | * "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'' | * "Vision Based Victim Detection from Unmanned Aerial Vehicles", M. Andriluka ''et al.'', IROS 2010. ''UAV, person detection'' | ||
Revision as of 10:58, 31 January 2018
Contents
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 |
|
| 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)
- End to End Learning for Self-Driving cars, Bojarski et al., 2016
- "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
- "Autonomous Door Opening and Plugging In with a Personal Robot", W. Meeussen et al., IROS 2010. PR2, grasping
Instructions for Homeworks
| Operating system |
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| Software |
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Schedule
Note: The blue squares in the "#" column below indicate Tuesdays.
| # | Date | Topic | Links/Readings/videos | Assignments/slides |
|---|---|---|---|---|
| 1 | Feb. 6 | Background, introduction to DARPA Robotics Challenge | ||
| 2 | Feb. 8 | DRC algorithm components | ARGOS challenge overview (8:00) | |
| 3 | Feb. 13 | Introduction to ROS | ETH ROS mini course (in particular: overview, RViz, TF), ETH case study "ANYmal at the ARGOS Challenge" | |
| 4 | Feb. 15 | PCL tutorial | ||
| 5 | Feb. 20 Register/add deadline Feb. 19 |
Plane/obstacle/object segmentation (3-D) | HW #1 | |
| 6 | Feb. 22 | HW #1 strategies | ||
| 7 | Feb. 27 | Image classification background | Stanford CS231n Image classification slides | |
| 8 | Mar. 1 | Introduction to convolutional neural networks | Stanford CS231n Convolutional Neural Networks slides HW #1 due | |
| 9 | Mar. 6 | Introduction to OpenCV (in ROS), deep learning libraries, detection & segmentation background |
Stanford CS231n Deep Learning software slides, Detection and Segmentation slides | |
| 10 | Mar. 8 | More on detection, tracking | YOLOv2, Redmon and Farhadi (CVPR 2017) | HW #2 |
| 11 | Mar. 13 | Localization | ETH localization lectures: 1 2 | |
| 12 | Mar. 15 | 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 |
| 13 | Mar. 20 | Motion planning background | HW #2 due | |
| 14 | Mar. 22 | Perception for stepping | "Learning Locomotion over Rough Terrain using Terrain Templates", M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009 | Paper presentation choice due Friday, March 23 |
| Mar. 27 | NO CLASS Spring break |
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| Mar. 29 | NO CLASS Spring break |
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| 15 | Apr. 3 | Reinforcement learning; project kick-off | ||
| 16 | Apr. 5 | DARPA Urban Challenge | Urban Challenge highlights, Stanford clips | |
| 17 | Apr. 10 Withdraw deadline Apr. 9 |
Student paper presentations | Project proposal due Monday, April 9 | |
| 18 | Apr. 12 | Student paper presentations | ||
| 19 | Apr. 17 |
Student paper presentations | ||
| 20 | Apr. 19 | Mid-project review; student paper presentation | ||
| 21 | Apr. 24 | Student paper presentations | ||
| 22 | Apr. 26 | Motion planning background | ||
| 23 | May 1 | Miscellaneous | ||
| 24 | May 3 | Final project review | ||
| 25 | May 8 | "Bonus" material | ||
| 26 | May 10 | Miscellaneous | ||
| 27 | May 15 | Final project presentations | Final project due |
