Instructor: Dr. Hong Zhang, 407 Athabasca Hall, 492-7188, hzhang@ualberta.ca
Course Page: http://www.cs.ualberta.ca/~zhang/c631
Lectures: Mondays and Wednesdays 10:30-11:50 AM, CSC B 41
Office Hour: Tuesday 10:00 - 12:00

Overview

This course is concerned with the subject of autonomous robot navigation. The students will become familiar with related mobile robotics research and study a number of classical and modern algorithms. Specifically, the course will focus on how a mobile robot builds a map and localizes itself in that map at the same time (the so-called SLAM problem), by making use of the information collected by its sensors such as laser range finders and cameras. The lectures will introduce both basic and advanced SLAM algorithms, and the students will gain an in-depth understanding of these algorithms by both reading research papers and examining their software implementations. Class lectures and homework assignments will rely on the Robot Operating System (ROS) - which provides libraries and tools to help software developers quickly create robot applications - to control robots in simulated environments and study SLAM algorithms on benchmark datasets.

Course Topics

• Introduction to robotics
• Robot Operating System (ROS)
• Coordinate frames, transformations, and robot kinematics
• Sensors: LiDARs, cameras, RGB-D, and IMU
• Odometry: wheel, visual and LiDAR odometry
• Filter-based SLAM algorithms
• Optimization-based SLAM algorithms
• Place recognition and loop closure detection
• Path planning and collision avoidance

Graduate student status in Computing Science or consent of the instructor; personal Linux (running Ubuntu 16.04) computer and familarity with installing and developing software (in either Python or C++) on Ubuntu.

• (9/4) SLAM Survey: [Cadena 2016], pp. 1-12 (1309-1320) and pp. 17-18 (1326-1327)
• (9/16) Basic linear algebra
• (9/16) Mobile robot kinematics
• (9/16) RTAB-Map [Labbe 2019], pp. 1-12 (416-427)
• (9/30) ORB-SLAM and ORB-SLAM2
• (10/2) Video Google [Sivc 2003].
• (10/7) Visual Odometry [Fraundorfer 2012].

• September 4, Introduction
• September 9, SLAM Overview
• September 11, RTAB-Map and ORBSLAM2 Demo
• September 16, Factor Graph Examples
• September 18
  - 2D/3D spatial transformations
  - Mobile robot kinematics
• September 23, LiDAR and LiDAR Odometry
• September 25, IMU and Odometry with IMU
• September 30, RGB-D Camera
• October 2, Visual Loop Closure Detection
• October 7, Project Description
• October 9, Visual Odometry
• October 16, PnP and Loop Closure Verfication
• October 21, Cost function in poseGraph SLAM
• October 23
  - Camera vs. image coordinate frame
  - Visual place recogntion with DL
  - graphSLAM via G2O
• November 6: Collective driving
• November 18: Particle filtering and Monte Carlo localization

• No.1 (Due Oct 2, 2019)
• No.2 (Due Oct 14, 2019)
  - Tutorial by Sean on installing and running RTAB-Map and ORB-SLAM2
• No.3 (Due Oct 28, 2019)
  - Answer to Q1

• Details

• Videos of Cool Robots

Readings

References

Evaluation

Student evaluation is based on four assignments (40%), one midterm (20%) and the course project (40%).

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