Week Starting 
Topic 
Reading Materials 
Movie List 
26 Aug 
Administration 
Nothing so far! 
Nothing so far! 
26 Aug 
Intellectual Context 
Nothing so far! 
Here is an intro lecture, with some remarks about safety. 
26 Aug 
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


31 Aug 
Image classification and simple detection 


7 Sep 
Safety lookout training 
No reading 

7 Sep 
Simple PID Control 
My PID control slides 

14 Sep 
ROS and PACMOD and Registration 


21 Sep 
Kalman and Particle Filters 


28 Sep 
Finish Particle Filters; Cameras and pairs of cameras 


9 Oct 
Finish pairs of cameras; flow and stereo; SFM, SLAM and visual odometry 


16 Oct 
EKFSLAM, FastSLAM, Direct methods 
 Slides on EKF SLAM
 Slides on FastSLAM
 Slides on Features and Interest points
 Slides on Direct SLAM
 Great notes on EKFSLAM by Joan Sola, with Matlab code, etc
 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 semidirect visual odometry) there is a pointer to code here, too.
 Christian Forster, Zichao Zhang, Michael Gassner, Manuel Werlberger, Davide Scaramuzza
SVO: SemiDirect Visual Odometry for Monocular and MultiCamera Systems
IEEE Transactions on Robotics, Vol. 33, Issue 2, pages 249265, Apr. 2017.
 Dense visual slam an earlier version of lsdslam for RGBD cameras, code too
 Dense Visual SLAM for RGBD Cameras (C. Kerl, J. Sturm and D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.


23 Oct 
Fall viruses and Semantic Segmentation 
 My semantic segmentation slides
 A simple twoclass CRF example with basic variational inference
 Fully connected CRFS

The Role of Context for Object Detection and Semantic Segmentation in the Wild
Roozbeh Mottaghi et al. (CVPR), 2014, pp. 891898
 Urban 3D Semantic Modelling Using Stereo Vision
Sunando Sengupta Eric Greveson, Ali Shahrokni2 Philip H. S. Torr, ICRA, 2013
 FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY
VARYING DENSITY
Timo Hackel, Jan D. Wegner, Konrad Schindler
 "Fully Convolutional Networks for Semantic Segmentation"
Jonathan Long∗ Evan Shelhamer∗ Trevor Darrell, 2014
 Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Philipp Krähenbühl, Vladlen Koltun
 The Fast Bilateral Solver
Jonathan T. Barron, Ben Poole

Fast highdimensional filtering using the permutohedral lattice.A. Adams, J. Baek, and M. A. Davis.
Computer Graphics Forum, 29(2), 2010. 2, 5
 The Stixel world: A mediumlevel representation of traffic scenes Marius Cordts, et al., ArXiv, 2017
 Towards a Global Optimal MultiLayer Stixel Representation of Dense 3D Data
David Pfeiffer, Uwe Franke, BMVC 2011
 GREIG, D., PORTEOUS, B., AND SEHEULT, A. 1989. Exact MAP
estimation for binary images. J. Roy. Stat. Soc. B. 51, 271–279.

