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Machine Learning for Robotics
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@srl.cit.tum.de

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teaching:w21:mobilerobotics [2021/07/09 11:22]
Prof. Dr. Stefan Leutenegger
teaching:w21:mobilerobotics [2021/07/09 11:22] (current)
Prof. Dr. Stefan Leutenegger
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 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: from perception to estimation, mapping, and control. As will also learn to work with different mathematical representations of robot states and maps. Since deployment in open-ended environments requires sophisticated perception, localisation, and mapping approaches, we will dedicate a substantial part of the course towards probabilistic multi-sensor-fusion and modern Simultaneous Localisation and Mapping (SLAM) and more general Spatial AI systems -- including elements of Machine Learning. In the last part, you will then be learning about how to use these representations of robot state and surroundings for navigation and control. 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: from perception to estimation, mapping, and control. As will also learn to work with different mathematical representations of robot states and maps. Since deployment in open-ended environments requires sophisticated perception, localisation, and mapping approaches, we will dedicate a substantial part of the course towards probabilistic multi-sensor-fusion and modern Simultaneous Localisation and Mapping (SLAM) and more general Spatial AI systems -- including elements of Machine Learning. In the last part, you will then be learning about how to use these representations of robot state and surroundings for navigation and control.
  
-===== Content =====+===== Contents =====
 **I Foundations:** **I Foundations:**
   * Representations of States, the Environment, and Uncertainty   * Representations of States, the Environment, and Uncertainty

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Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@srl.cit.tum.de

Follow us on:
SRL  CVG   DVL