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SLAM-Claus_Brenner-Youtube

This repository contains solution for SLAM lectures taught by Claus Brenner on YouTube.

Updates

  1. In addition to the cylinder based features for map correspondence I have also added corner based features for map correspondences.
  2. I have added Unscented Kalman Filter Loacalization in addition to the extendend kalman filter taught in the course.

Requirements

  • Python 2
  • Matplotlib
  • Pylab

It is best to have either Anaconda or miniconda installation on your system, as it should satisfy all the requirements by default.

Instructions to run the solutions

Contents of the Lecture

1) Motion Modeling and Feature engineering

2) Pose estimation with Scan Matching

3) Bayes filter and 1-D Kalman fiter position tracking

4) Extended Kalman Filter Localization

5) Unscented Kalman Filter Localization

6) Particle Filter Localization

7) Extended Kalman Filter SLAM

8) Particle Filter SLAM

9) Path Planning