Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 1.1: Welcome Jürgen Sturm Technische Universität München

Course Goal  How can we enable a quadrocopter to fly autonomously?  How can we estimate its state from its sensor readings?  How can we generate control commands to move it towards its goal? Jürgen Sturm Autonomous Navigation for Flying Robots 2

Control Architecture Jürgen Sturm Autonomous Navigation for Flying Robots 3 Localization and Mapping Robot Sensors Actuators Physical World Forces Torques Position Velocity Acceleration Kinematics Dynamics Position Control Trajectory Attitude EstimationAttitude Control RPM EstimationMotor Speed Control Planner Image Credit: Parrot

Course Content by Week 1.Introduction, state-of-the-art 2.Linear algebra, 2D geometry 3.3D geometry and sensors 4.Motors and motor controllers (PID) 5.Probabilistic state estimation 6.Bayes and Kalman filters 7.Visual odometry 8.Cutting edge research results Jürgen Sturm Autonomous Navigation for Flying Robots 4

Course Organization  Course duration: 8 weeks  Video lectures  minutes per week  Interactive exercises  Quizzes, arithmetic problems  Hands-on programming exercises in Python Jürgen Sturm Autonomous Navigation for Flying Robots 5

Upcoming Next Lecture 1.2:  Why quadrotors?  Potential applications Jürgen Sturm Autonomous Navigation for Flying Robots 6