|Course coordinators||Raghu Krishnapuram and Chiranjib Bhattacharyya|
|Background||Autonomous navigation is an actively researched area around the globe, with many large companies making large investments. Autonomous robots such as self-driving cars and drones are set to be game changers in many fields such as infrastructure maintenance, transportation, public safety, disaster response, agriculture, mining, health care, and exploration. Autonomous navigation lies at the heart of autonomous robots, and involves a multi-disciplinary approach. It includes a variety of topics (e.g., sensor technologies, behaviour modelling, trajectory prediction, localization and mapping methods, and path planning in the presence of obstacles) that come from subject areas such as computer vision, machine learning, artificial intelligence, robotics, estimation theory, control theory, and optimization. This course will cover the main theoretical concepts and practical approaches to autonomous navigation through a combination of lectures, assignments and projects.|
|Topics covered||Geometry, odometry, sensors, state estimation methods (Kalman filter, unscented Kalman filter, particle filtering), camera modelling and calibration, structure from motion, visual motion estimation, feature tracking, robust estimation, motion planning, obstacle avoidance, and exploration.|
|Prerequisites||Knowledge of random processes or probability and statistics, linear algebra, and data structures and algorithms will be assumed. In addition, familiarity with basic optimization methods, algorithm design, basics of machine learning and computer vision is needed.|
|Credit hours||This will be a 2:1 credit course, where hands-on work will be emphasized. The class will meet twice a week for a duration of 1.5 hours each.|
|Assessment||Six assignments will account for 50% of the grade. There will be a mid-term examination, which will account for 25% of the grade. There will also be a group project with an oral presentation/exam, which will account for 25% of the grade.|
|Reference textbooks||Sebastian Thrun, Wolfram Burgard, and Dieter Fox, Probabilistic robotics, MIT Press, 2006.
Richard Hartley and Andrew Zisserman, Multiple view geometry, Cambridge University Press, 2003.
Richard Szeliski, Computer vision: Algorithms and applications, Springer, 2010.
|Interested?||Please attend the organizational meeting to finalize class hours and answer other questions to be held on 6 August 6 2018 at 8:30 am in the Seminar Hall at the Robert Bosch Centre, 3rd Floor, Entrepreneurship Building, SID [Map].|