Repositary for Localization Course
Course link: https://www.udemy.com/course/autonomous-robots-localization/
Required Packages
python=3.7.4
numpy=1.16.4
matploblib=3.1.0
-> You don´t know which measuremnet corespond to which pole.
-> Based on cost function, you will find the location with lowest total cost.
-> Cost function: (Minimal total distance diference between guess location and pole measurements)**2
-> Bayes rule used to determinate location of robot in 1D world
-> Bayes rule is used to update probability of some phenomena based on new observation or in other words update your beliefs based on evidence
-> Bayes rule: https://en.wikipedia.org/wiki/Bayes%27_theorem or https://www.youtube.com/watch?v=HZGCoVF3YvM
-> 2-1 -> Robot moves every time exactly same distance with 0 uncertainty
-> 2-2 -> Robots moves with some move uncertainty
-> Using Particle filter to determinate location of robot in 1D world
-> Particle filter used multiple particles (virtual copy of robot) to determinate it position.
In every loop particles will:
1. Move -> movement of particles is identical to real movement of robot. Only difference is that every particle moves with some uncertainty which is based on gaussian distribution
2. Measure -> Robot and particles scan its environment. Measurement has also some uncertainty
3. Update -> Based on difference between what robot sees and particle sees weights are calculated.
4. Resample -> Some portion of particles with the highest weights will be choosen to be fundamental, and rest of the particles will be resampled around these fundamentals ones.
-> Using Particle filter to determinate location of robot in 2D world