Tartalom
  1. Linkek
    1. Linux ismeretek
    2. Robotikai ismeretek
      1. Stanford University
      2. TU München
    3. Magyar nyelvű ROS oktatóanyagok
    4. Angol nyelvű ROS oktatóanyagok
      1. Apex AI
      2. ETH Zürich
      3. University of Bonn
      4. Articulated Robotics
      5. The Robotics Back-End
      6. Foxglove ROS 2 tutorials
      7. Official ROS 2 documentation
      8. Egyéb

Linkek

Linux ismeretek

  • Egyszerű linux parancsok - [hun]
  • ROS training - 0.1 Intro to Ubuntu GUI - [eng]
  • ROS training - 0.2 The Linux File System - [eng]
  • ROS training - 0.3 Using the Terminal - [eng]
  • Terminal kezelése - [hun]
  • 60 Linux Commands you need to know [video, eng]
  • The 50 Most Popular Linux & Terminal Commands, freeCodeCamp [video, eng]
  • Git kezelése:
    • Beadandó leírás, Git és GitHub - [hun]
    • GitHub first-contributions magyar fordítás - [hun]
    • GitHub Learning Lab - [eng]
  • VS code - [hun], [vid]
  • Python [hun]
  • Python freeCodeCamp video [eng]
  • Python 60 days with python videos [eng]
  • C++ [hun]

Ezek nagy részét a legegyszerűbb elsajátítani a Raspberry Pi-hez készült segédanyaggal: github.com/horverno/sze-academic-rpi.

Robotikai ismeretek

  • Alap robotikai ismeretek [eng]
  • Kálmán filter [eng]
  • Universitat Politècnica de Catalunya BarcelonaTech (UPC) - [eng]
  • Pozna Tanár Úr és Antonya Csaba által írt angol nyelvű oktatóanyag - Autocarsim [eng]

Stanford University

TU München

Number Session Description Video Lecture Slides
1 Python intro Some basics of programming in python for beginners. ResearchGate    
2 Basics of mapping and localization Exemplary implementation of a Kalman filter and application for localization via GNSS-signal. YouTube ResearchGate
3 SLAM The google cartographer SLAM algorithm is applied to data from the KITTI-dataset. Note, that this lecture is held in Linux and has its own dependencies, please refer to the local readme. YouTube ResearchGate
4 Detection Overview about the YOLO-approach from network architecture to exemplary usage. YouTube ResearchGate
5 Prediction Implementation of the pipeline to setup a motion prediction algorithm based on a Encoder-Decoder architecture. YouTube ResearchGate
6 Global plannings A global optimal race line optimization is shown. This lecture has its own dependencies, please refer to the local readme. YouTube ResearchGate
7 Local planning A local planning algorithm based on a graph-based approach is presented. YouTube ResearchGate
8 Control The design of a velocity controller and numerical solver for differential equation are covered. YouTube ResearchGate
9 Safety assessment The evaluation of the criticality of planned trajectories based on various metrics and their sensitivity is discussed. YouTube ResearchGate
10 Teleoperated driving How to send and receive data via MQTT over network is shown in this practice session. YouTube ResearchGate
11 End-to-End The exemplary pipeline of data collection from expert demonstration, training and application are treated in this session. This lecture has its own dependencies, please refer to the local YouTube ResearchGate

Magyar nyelvű ROS oktatóanyagok

Angol nyelvű ROS oktatóanyagok

Apex AI

ETH Zürich

2021
Topics
Material
22.02.
  • ROS architecture & philosophy
  • ROS master, nodes, and topics
  • Console commands
  • Catkin workspace and build system
  • Launch-files
  • Gazebo simulator
  • Programming Tools
24.02.
  • ROS package structure
  • Integration and programming with Eclipse
  • ROS C++ client library (roscpp)
  • ROS subscribers and publishers
  • ROS parameter server
  • RViz visualization
26.02.
  • TF Transformation System
  • rqt User Interface
  • Robot models (URDF)
  • Simulation descriptions (SDF)
01.03.
  • ROS services
  • ROS actions (actionlib)
  • ROS time
  • ROS bags
  • Debugging strategies
  • Introduction to ROS2
05.03.
  • Case study: Using ROS in complex real-world applications
2021:
2020:
2018:
2017:

University of Bonn

Link: ipb.uni-bonn.de/sdc-2021

Self-Driving Cars: An Introduction (Cyrill Stachniss) Introduction lecture for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. A course by Cyrill Stachniss, Jens Behley, Nived Chebrolu, Benedikt Mersch, Igor Bogoslavskyi.

Self-Driving Cars: Localization (Daniel Wilbers) Localization lecture for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. Further Information on Deep Learning and CNNs, see our Machine Learning for Robotics and Computer Vision Course: Youtube Link

Self-Driving Cars: Control (Nived Chebrolu) Control lecture for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. A course by Cyrill Stachniss, Jens Behley, Nived Chebrolu, Benedikt Mersch, Igor Bogoslavskyi. Youtube Link

Model Predictive Control – Part 1: Introduction to MPC (Lasse Peters) Introduction to Model Predictive Control; lecture presented by Lasse Peters. Youtube Link

Model Predictive Control – Part 2: Numerical Methods for MPC (Lasse Peters) Numerical Methods for Model Predictive Control; lecture presented by Lasse Peters. Youtube Link

Self-Driving Cars: Planning (Benedikt Mersch) Planning lecture for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. A course by Cyrill Stachniss, Jens Behley, Nived Chebrolu, Benedikt Mersch, Igor Bogoslavskyi. Youtube Link

Self-Driving Cars: Behavior Estimation (Benedikt Mersch) Behavior estimation lecture for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. A course by Cyrill Stachniss, Jens Behley, Nived Chebrolu, Benedikt Mersch, Igor Bogoslavskyi, Lasse Peters. Youtube Link

Self-Driving Cars: Perception – Part 1 (Jens Behley) Youtube Link Perception lecture for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. Further Information on Deep Learning and CNNs, see our Machine Learning for Robotics and Computer Vision Course: Youtube playlist

Self-Driving Cars: Perception – Part 2 (Jens Behley) Perception lecture for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. Youtube Link

Self-Driving Cars: View from Practice (Igor Bogoslavskyi) Lecture giving a view from practice for the course “Techniques for Self-Driving Cars” taught at the University of Bonn. A course by Cyrill Stachniss, Jens Behley, Nived Chebrolu, Benedikt Mersch, Igor Bogoslavskyi. Youtube Link

Articulated Robotics

ROS 2

The Robotics Back-End

ROS 2

Foxglove ROS 2 tutorials

Official ROS 2 documentation

Egyéb