CSC477: Introduction to Mobile Robotics, Fall 2019
OverviewThis 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.
Office: DH3066, UTM
Office Hours: Thu 4-5pm
Office: DH2085, UTM
Office Hours: Tue 2-3pm
Lectures: Thursdays, DH4001, UTM, 5-7pm
Tutorials and Practicals: Wednesdays, DH2020, UTM, 1-2pm
Discussion board and announcements will take place on Quercus
Anonymous feedback form for suggested improvements
Course DescriptionThis 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 state-(in)dependent 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?
- The 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?
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
Frames of reference. Rotation representations. Homogeneous coordinates and transformations. Rigid body motion.
Dynamical systems and control. Examples: Dubins car, differential drive car, unicycle, pendulum, cartpole, quadcopter. Holonomic vs. non-holonomic systems.
|Intro to ROS||pdf,
Paul Furgale: robot pose
Dudek & Jenkin 3.1.5,6
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||
||Optional: Astrom and Hagglund, Ch. 2
Lavalle Ch. 8.4
Dudek & Jenkin 6.3.4
Optional: Howie Choset's notes
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|
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
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
Maximum Likelihood, Least Squares Estimation, Maximum A Posteriori Estimation
Least squares as a special case of maximum likelihood estimation on Gaussian models.
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
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
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
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
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
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
Sanja Fidler's notes on depth from stereo
Study break, beginning of exams
||Review for final exam|
- Assignment #1, PID control for wall following and introduction to ROS, due Oct 7, 6pm EST.
- Assignment #2, Path planning with A*, RRT, and LQR control, due Nov 1, 6pm EST.
- Assignment #3, Occupancy grid mapping, EKF and least-squares localization, due Nov 18, 6pm EST.
- Assignment #4, Particle filters and Monte Carlo localization. Bonus: depth from stereo, and racecar deployment of A1, due Dec 4, 6pm EST.
- 4 assignments worth 15% each = 60%
- 5 (out of 7) quizzes worth 2% each = 10%
- 1 final exam worth 30%
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