CPS 196/296: Robotics
Spring 2007

Course information
Schedule
Links


Date Topic Homework Handouts and Slides Supplemental Material
Sensing Technologies
  • Range Finders
  • Sonar
  • Cameras
 Intro Slides
Robotics Overview
Sensor Overview		 Canesta Single Chip Depth Sensor
Solid State Image Sensor Overview
  • CCD Technology
  • CMOS sensor technology
  • Gamma Curves
  • Statistics Review
  • Understanding Sensor Noise
 Read: Kodak's CCD Primer and doc on the conversion of photons to charge;
Also read handouts on sensor noise and CMOS sensors.
Homework 1 due 2/8.
Review Poynton's Gamma FAQ then review it again after lecture.
 Intro to Image Sensors
Intro Stats and Probability
Sensor Noise
Gamma Curves
ISO Boosting		 Nikon Microscopy's CCD Fundamentals
Horizontal Offset vs. Depth Experiment: 1 2 3
Dark Current Test Images
Shot Noise Test Images
Sensing Color
  • Linear Algebra Review
  • What is Color?
  • How is color sensed?
  • Color Spaces
  • Color Interpolation
  • The Foveon X3 Sensor
 Read Poynton's Color FAQ
Read: Bayer's Patent
Read: Interpolation Algorithm Survey
Read: Foveon X3 Sensor Paper		 Color Introduction
Colorspaces Slides
Color Sensing Slides		 Foveon X3 Patent
Sigma SD 10 Review
Subsampling Experiments
Optics Overview
  • Lens Equations
  • Scene Reconstruction
  • Stereo Vision
 Read Stereo Vision Overview Basic optics slides
Your Camera as a Matrix
Stereo Vision Slides		 Middlebury Stereo Vision Page
Schaeffer & Parr Paper
Tracking
  • HMMs
  • Kalman Filter
  • Extended Kalman Filter
  • Unscented Filter
  • Particle Filter
  • Rao Blackwellized Particle Filter
  • Robot Localization
 Read Rabiner HMM Tutorial
Read Homework 2 due 3/1
Read Negenborn Master's Thesis Chapters 1-4
Read Negenborn Master's Thesis Chapter 5
Read Particle Filters in Robotics
Unscented Filter Paper
Read Mixture Kalman Filter Paper (AKA Rao-Blackwellized Filtering)		 HMM slides
Kalman Filter Slides
Filtering Tricks Slides
Particle Filters for Localization		 Images for Homework 2
Regression Slides
About sRGB
Graphical Model Overview
  • Conditional Independence
  • Inference & Sampling
 Read Bayesian Networks without Tears
 Bayes Net Intro Slides
Mapping Overview
  • Off-line mapping
  • On line mapping (SLAM)
    • Scan Matching
    • Kalman Filter Mapping
    • FastSLAM
    • DP-SLAM
 Read Probabilistic mapping of an environment by a mobile robot
Read Estimating Uncertain Spatial Relationships in Robotics
Read Incremental Mapping of Large Cyclic Envrionments
Read Bayesian Map Learning in Dynamic Environments
HW 3 DUE 3/27
Read FastSlam paper
Read DP-SLAM
HW 4 Due May 2		 Introduction to SLAM slides
DP-SLAM Slides		 Mike Montemerlo's Disserstation
Thin Junction tree Filters
Learning Motion Models
Motion Planning Overview (Guest Lecture by Jeff Phillips on Tuesday March 20.) Read Probabilistic roadmaps for path planning in high-dimensional configuration spaces (free download from within Duke)
Read LaValle Book sections 5.4-5.6
Micro Robotics (Guest Lecture by Bruce Donald) Read An Untethered Electrostatic Globally Controllable MEMS Micro-Robot MEMS web page
MEMS movies
How to Give a Bad Talk Bad Slides
Advanced Topics (Student Presentations) Suggested Papers
Presentation Schedule