This repository contains all the resolved coding exercises from the SLAM and PATH PLANNING course given by the professor Claus Brenner from the University of Leibniz. All the exercises have been coded in the Python 2.x programming language. All the exercises have been double or triple checked to ensure that they are well done. It may be possible that if you try to address these coding challenges without the theoretical background, they might be a pretty difficult challenge. For a complete description of all the theoretical aspects of this course, please refer to the professor Brenner's YouTube playlist This is an amazing course pretty well structured and complete. It covers since the beginner steps to very sophisticated algorithms. Previous algebra and statistical concepts are desired to follow the explanations in a straight way. It's also desirable to have, at least, basic knowledge about the Python programming.
In this repository I've attached my personal notes. During the course I've been writing notes that later on I used in several occasions to create PDF documents. Some of these PDF documents are quite explicative and others are just reminders of the concepts explained in the video lectures. I've created my docs in LaTeX format, so I thought that maybe these source files could be interested in others to create its own notes based on them. In each folder of this repository you'll find three other folders that contain the CODE for that lecture, my private NOTES (if they exist) in lyx format, tex format and pdf format, and the FIGURES folder. I've invested an enormous amount of time creating vectorial graphics to illustrate some theoretical concepts. I also thought that maybe these pictures could be useful to the community, so you are welcomed to use them if you find them useful. Please, consider that these notes are my personal notes and maybe my writing style is not yours.
I also created two YouTube playlists in my channel to illustrate how these algorithms should work. One playlist is for the SLAM algorithms (KF, EKF, PF, Fast-SLAM) and the other playlist is for the path planning algorithms. So, you are also welcomed to visit these playlists to compare your solutions with mine.