CSC477: Introduction to Mobile Robotics, Fall 2020
Overview
This course provides an introduction to robotic systems from a computational perspective. A robot is regarded as an intelligent computer that can use sensors and act on the world. We will consider the definitional problems in robotics and look at how they are being solved in practice and by the research community. The emphasis is on algorithms, probabilistic reasoning, optimization, inference mechanisms, and behavior strategies, as opposed to electromechanical systems design. This course aims to help students improve their probabilistic modeling skills and instill the idea that a robot that explicitly accounts for its uncertainty works better than a robot that does not.Course Details
Teaching Assistant
y@mail.utoronto.ca, y=homanga.bharadhwaj
Office Hours: Fri, 2-3pm ET
Lectures: Wed, 3-5pm ET
Tutorials and Practicals: Wed, 5-6pm and 6-7pm ET
Zoom links have been posted on Quercus
Announcements will be posted on Quercus
Discussions will take place on Piazza
Anonymous feedback form for suggested improvements
Course Description
This course will broadly cover the following areas:- Kinematics and Dynamics: how can we model robotic systems using approximate physical models that enable us to make predictions about how robots move in response to given commands?
- Feedback Control and Planning: how can we compute the commands that can bring a robotic system from its current state to a desired state?
- Mapping: how can we combine noisy measurements from sensors with the robot’s pose to build a map of the environment?
- State Estimation: the state of the robot is not always directly measurable/observable. How can we determine the relative weighs of multiple sensor measurements in order to form an accurate estimate of the (hidden) state?
- Geometry of Computer Vision: how can modeling pixel projections on an RGB camera help us infer the 3D structure of the world? How can we triangulate points seen from two cameras? How can we estimate the camera’s pose (and therefore the robot’s) while it is moving in the environment?
Schedule
Week | Date | Lecture | Tutorial/Practical | Slides | Reading |
---|---|---|---|---|---|
1 | Sep 9 |
Introduction Motivation, logistics, rough description of assignments, sense-plan-act paradigm. Syllabus, Quiz 0 (Introduction, Background, Expectations) Sensors and Actuators Camera, LiDAR, tactile, IMU, depth, GPS, Hall-effect sensors, encoders, RGBD. Pulse-Width Modulation. Motors. |
None | Dudek & Jenkin 3.1.1,4, 3.2-3, 4.1-8, 4.10, 5.1.1 Optional: Mike Langer's notes |
|
2 | Sep 16 |
Kinematics Frames of reference. Rotation representations. Homogeneous coordinates and transformations. Rigid body motion. Dynamics Dynamical systems and control. Examples: Dubins car, differential drive car, unicycle, pendulum, cartpole, quadcopter. Holonomic vs. non-holonomic systems. |
Intro to ROS | pdf,
pptx |
Paul Furgale: robot pose Lavalle 13.1 Dudek & Jenkin 3.1.5,6 |
3 | Sep 23 |
PID Control Tuning, cascading PID, advantages and drawbacks. Artificial Potential Fields and Obstacle Avoidance Implementation issues, navigation functions. Vector-field histogram (VFH), dynamic window approach (DWA). |
Linear algebra refresher |
pdf,
pptx |
Optional: Astrom and Hagglund, Ch. 2 Lavalle Ch. 8.4 Dudek & Jenkin 6.3.4 Optional: Howie Choset's notes |
4 | Sep 30 |
Linear Quadratic Regulator (LQR) Computing optimal actions for linear dynamical systems with quadratic cost-to-go functions. |
Probability refresher | pdf, pptx, code | Optional: Stephen Boyd's LQR notes and examples |
5 | Oct 7 |
Planning Dijkstra, A*, Rapidly-exploring Random Trees (RRT), Probabilistic RoadMaps (PRM) |
Intro to numpy, Implementing LQR | pdf, pptx | Blog post on A* Udacity Lesson 4 Lavalle 5.5, 5.6 |
Oct 14 |
Reading week |
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6 | Oct 21 |
Map Representations and Map Alignment Occupancy grids, Octrees, Voronoi Graph, Homotopy Classes. Map alignment with known or unknown correspondences. Iterative Closest Point (ICP). Occupancy Grid Mapping With Known Robot Poses Log-odds ratio, Probabilistic dynamics and measurement models, Bayesian estimation. |
Derivations for occupancy grid mapping | pdf, pptx |
Pieter Abbeel's notes
Pieter Abbeel's notes Probabilistic Robotics Ch. 2 and Ch. 9 |
7 | Oct 28 |
Maximum Likelihood, Least Squares Estimation, Maximum A Posteriori Estimation Least squares as a special case of maximum likelihood estimation on Gaussian models. GraphSLAM Expectation and Covariance. Geometric interpretation of the covariance matrix. Nonlinear Least Squares formulation of the Simultaneous Localization And Mapping (SLAM) problem. |
Demonstrating gMapping | pdf, pptx | Udacity Lesson 6 Probabilistic Robotics Ch. 11 |
8 | Nov 4 |
Kalman Filter Bayes' rule on Gaussian distributions. Example of 1D Kalman Filter. Bayes' Filter and Kalman Filter Kalman Filter as a special case of Bayes' Filter. Examples of 2D and 4D Kalman Filter. General prediction and update equations. |
pdf, pptx | Udacity Lesson 2 Kalman Filter, Illustrated, Probabilistic Robotics Ch. 2,3 |
|
9 | Nov 11 |
Extended Kalman Filter (EKF) Bayes' Filter and nonlinear transformations. Monte Carlo sampling vs. Linearization. EKF prediction and update equations. Examples: EKF Localization and EKF SLAM. |
Basic Kalman Filter implementation | pdf, pptx | Cyrill Stachniss' intro to EKF Cyrill Stachniss' intro to EKF-SLAM Probabilistic Robotics Ch. 2,3 |
10 | Nov 18 |
Particle Filter Representing multimodal distributions. Particle propagation and resampling. Pathologies of particle filter. Importance Sampling. Examples: Markov localization in a known map. FastSLAM. |
pdf, pptx | Udacity Lesson 3 Optional: Thrun's paper on PF |
|
11 | Nov 25 |
Camera Optics and Multi-view Geometry Pinhole cameras, lenses, perspective projection. Aperture, focal length, exposure time, depth-of-field. Structure from Motion. |
pdf, pptx | Optional: James Tompkin's notes. Sanja Fidler's notes on depth from stereo |
|
12 | Dec 02 |
Visual odometry and Visual SLAM Epipolar constraints. Depth from stereo disparity for parallel cameras. Triangulation as a least-squares problem. Scale issues in visual odometry with a single camera. Visual SLAM vs. structure from motion. |
pdf, pptx | Optional: James Tompkin's notes on stereo and
SfM. Sanja Fidler's notes on depth from stereo |
|
13 | Dec 9 |
Study break, beginning of exams |
Review for final exam |
Assignments
- Assignment #1, PID control for wall following and introduction to ROS, due Oct 7, 3pm ET.
- Assignment #2, Path planning with A*, RRT, and LQR control, due Oct 28, 3pm ET.
- Assignment #3, Occupancy grid mapping, EKF and least-squares localization, due Nov 18, 3pm ET.
- Assignment #4, Particle filters and Monte Carlo localization. Bonus: depth from stereo, and racecar deployment of A1, due Dec 8, 3pm ET.
Marking scheme
- 4 assignments worth 15% each = 60%
- 5 (out of 7) quizzes worth 3% each = 15%
- 1 final exam worth 25%
Recommended, but optional, textbooks
- Probabilistic Robotics, by Thrun, Fox, and Burgard
- Planning Algorithms, by Lavalle
- Robotics, Vision, and Control, by Corke
- Computational Principles of Mobile Robotics, 2nd edition, by Dudek and Jenkin
- State Estimation for Robotics, by Barfoot
- Bayesian Filtering and Smoothing, by Sarkka
- Introduction to Autonomous Mobile Robots, by Siegwart, Nourbakhsh, Scaramuzza
- (Chapters 2 and 4 from) Computer Vision: Models, Learning, and Inference, by Prince
Related courses
- Pieter Abbeel's course
- Sebastian Thrun's Udacity course
- Related sections from James Tompkin's vision course
- Related sections from Sanja Fidler's vision course
- Related sections from Stephen Boyd's linear systems course
- Related sections from Russ Tedrake's underactuated robotics course