UIUC CS-588 Autonomous Vehicle System Engineering Outline

This page contains what we've done


Mediaspace Channel

The class recordings appear here, and should be accessible to you. When I remember to wear the microphone, you can hear the lecture, too!

Week Starting Topic Reading Materials Movie List
25 Aug Admin Nothing so far! These are 2020 movies; the 2021 movies are on mediaspace.

but you might find you prefer these, or it may amuse you to check whether camera geometry has changed over the last year, etc.

25 Aug Safety
  • Movie of the first day goes here
25 Aug Intellectual Context Intro lecture recording will go here.
25 Aug (Y) Basic Convolutional Neural Networks
  • Deep nets: Units; AML: 16.1
  • Deep nets: Gradients; AML: 16.2
  • Deep nets: Backpropagation; AML 16.3
  • Deep nets: Gradient tricks; AML 16.4
  • Deep nets: Convolutional layers; AML 17.1
  • Deep nets: More on convolutional layers; AML 17.1, 17.2
27 Aug Safety lookout training
27 Aug Image classification and simple detection
  • Basic object detection: AML 19
WE 3 Sep Simple PID Control
WE 10 Sep Point set registration, ROS, PACMOD and the simulator I'm away 10 Sep
WE 17 Sep Finish point set registration; Kalman, extended Kalman and particle filters
WE 24 Sep Particle Filters; Cameras and pairs of cameras I'm away 24 Sep

Below is a video lecture in lieu. This does cameras and homogeneous coordinates and camera matrices in some detail. I'll do pairs of cameras in person.

Slides and references
WE 1 Oct Pairs of cameras; flow and stereo; SFM, SLAM and visual odometry
  • Slides on pairs of cameras (slightly revised)
  • Multiple View Geometry in Computer Vision Second Edition Richard Hartley and Andrew Zisserman, Cambridge University Press, March 2004.
  • Y. Boykov, O. Veksler and R. Zabih (2001), "Fast approximate energy minimisation via graph cuts", IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1222–1239.
  • Li Hong and G. Chen, "Segment-based stereo matching using graph cuts," Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., Washington, DC, USA, 2004, pp. I-I, doi: 10.1109/CVPR.2004.1315016.
  • R. Szeliski et al., "A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 6, pp. 1068-1080, June 2008, doi: 10.1109/TPAMI.2007.70844.
  • T. Brox, C. Bregler and J. Malik, "Large displacement optical flow", CVPR 2009
  • High Accuracy Optical Flow Estimation Based on a Theory for Warping, Thomas Brox, Andr´es Bruhn, Nils Papenberg, and Joachim Weickert, ECCV, 2004
  • My slides on very simplest SFM and SLAM
  • My slides on basic visual odometry
  • Tutorial on visual slam at CVPR2014

    I found the following particularly helpful

WE 8 Oct EKFSLAM, FastSLAM, Direct methods
WE 15 Oct finish EKFSLAM, Direct methods, FAST SLAM (movie); start path planning
  • Direct methods
    • Slides on Direct SLAM
    • Christian Forster, Zichao Zhang, Michael Gassner, Manuel Werlberger, Davide Scaramuzza SVO: Semi-Direct Visual Odometry for Monocular and Multi-Camera Systems IEEE Transactions on Robotics, Vol. 33, Issue 2, pages 249-265, Apr. 2017.
    • Dense visual slam an earlier version of lsd-slam for RGB-D cameras, code too
    • Dense Visual SLAM for RGB-D Cameras (C. Kerl, J. Sturm and D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.
    • Slides on FastSLAM
    • Slides from Ben Kuipers on FastSLAM
    • Slides from Burgard et al on FastSLAM
    • FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem Michael Montemerlo and Sebastian Thrun and Daphne Koller and Ben Wegbreit AAAI 02
    • FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges Michael Steven Montemerlo and Sebastian B Thrun and Daphne Koller and Ben Wegbreit IJCAI -03
    • lsdSLAM website - there is a pointer to a github site with code here.
    • SVO (fast semi-direct visual odometry) there is a pointer to code here, too.
WE 22 Oct Motion planning
WE 29 Oct Lane boundaries to scenes
WE 5 Nov finish Semantic segmentation
12 Nov Weather
19 Nov Intrinsic images and ground maps
3 Dec Learning to control