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  • Technical University of Darmstadt
  • Germany,Darmstadt

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Verneinender/README.md

Hello!

Experience:

  • Research assitant at Technical University of Darmstadt.

Education:

  • Master of Science in Mechanical Engineering at Technical University of Darmstadt. (not finished)
  • Bachelor in Mechanical Engineering at Jiangsu University.

GitHub Stats:

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  1. Mobile_Robotics_Exercises Mobile_Robotics_Exercises Public

    This repository contains all the exercises from the course: Introduction to Mobile Robotics (University of Freiburg: IAS). They are all written in Python.

    Python 1

  2. visual-slam-exercises visual-slam-exercises Public

    This repository contains all exercises from the course: Basic Knowledge on Visual SLAM: From Theory to Practice. All the exercises are written in C++.

    C++ 1

  3. Robot_Mapping_Exercises Robot_Mapping_Exercises Public

    This repository contains all the exercises from the course: Robot Mapping (University of Freiburg: IAS). All exercises are written in Octave.

    MATLAB

  4. socket socket Public

    This repository is used to learn socket programming. It contains some frameworks that are used to communicate with TCP/IP and UDP.

    C++

  5. Sensor-Fusion-for-Localization-exercises Sensor-Fusion-for-Localization-exercises Public

    This repository contains all exercises of the course: Sensor Fusion for Localization. The goal of this course is to build a localization system with IMU, Lidar, Camera, and Odometry.

    C++ 6 1

  6. IMU-GNSS-Lidar-sensor-fusion-using-Extended-Kalman-Filter-for-State-Estimation IMU-GNSS-Lidar-sensor-fusion-using-Extended-Kalman-Filter-for-State-Estimation Public

    Forked from diegoavillegasg/IMU-GNSS-Lidar-sensor-fusion-using-Extended-Kalman-Filter-for-State-Estimation

    State Estimation and Localization of an autonomous vehicle based on IMU (high rate), GNSS (GPS) and Lidar data with sensor fusion techniques using the Extended Kalman Filter (EKF).

    Python