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+ | ====== Mobile Robotics ====== | ||
+ | **Lecture (2h) and Exercise (2h) (5 ECTS)** | ||
+ | |||
+ | Mobile robots have been sent to Mars, can vacuum clean our homes, mow the lawn, and are promised to carry us around in the form of self-driving cars or even flying taxis. In this module, you will learn about the different component of such mobile robots and their interactions: | ||
+ | |||
+ | ===== Contents ===== | ||
+ | **I Foundations: | ||
+ | * Representations of States, the Environment, | ||
+ | * Overview of Sensors and Actuators | ||
+ | |||
+ | **II Multi-Sensor Estimation: | ||
+ | * Probabilistic Nonlinear Least Squares | ||
+ | * Kinematics and Temporal Models | ||
+ | * Recursive Estimation | ||
+ | |||
+ | **III From SLAM to Spatial AI:** | ||
+ | * Sparse Visual and Visual-Inertial SLAM | ||
+ | * Dense Mapping and SLAM | ||
+ | * Semantic, Object-level and Dynamic Mapping | ||
+ | |||
+ | **IV Planning and Control:** | ||
+ | * Feedback Control, Model-Based, | ||
+ | * Motion Planning and Exploration | ||
+ | * Reinforcement Learning: Relation to Models, Control, and Perception | ||
+ | |||
+ | ===== Prerequisites ===== | ||
+ | Passion for mobile robots and drones, as well as solid mathematical foundations regarding analysis, linear algebra, and probability theory. In parallel to this course, “Mobile Robotics Practicals” is offered, the participation in which is highly recommended. Additionally, | ||
+ | * Computer Vision II: Multi-View Geometry (IN2228), | ||
+ | * Robotics (IN2067), | ||
+ | * Robot Motion Planning (IN2138), | ||
+ | * Introduction to Deep Learning (IN2346) | ||
+ | * Motion planning for autonomous vehicles (IN2106, IN0012, IN4221) | ||
+ | The exercises will require solid knowledge of Python. | ||
+ | |||
+ | ===== Literature ====== | ||
+ | * Lecture slides | ||
+ | * [[http:// | ||
+ | |||
+ | ===== General ===== | ||
+ | * Lecturer: Prof. Dr. Stefan Leutenegger | ||
+ | * Teaching Assistants: Dr. Xingxing Zuo, Simon Boche, | ||