Monte Carlo Filter using python

When we talk about robotics and Autonomous Vehicles, one important problem arises, which is the localization problem. Let's take the self driving car as an example, The car needs to know its position in the world it operates in, this is very important, especially when it comes to navigation and path finding. A number of algorithms exist to solve this problem, one of them is Monte Carlo Localizer or Monte Carlo Filter. Monte Carlo Filter uses statistics with the help of sensing and moving operations to find the location that have the maximum probability that the robot is at.
Monte Carlo Filter is used in many real world examples where the world is represented as 2D Matrix,
To simplify the algorithm, I implemented it in a 1D world using python, You can find the code at GitHub following this link. I also recorded the implementation process and uploaded it to YouTube.
The Video:

Comments

Post a Comment

Popular posts from this blog

Step Wise Project Planning Example

Image
Step Wise is a planning method used in software project management. Step Wise is compatible with PRINCE 2 which is a set of project management standards published by the Central Computing and Telecommunications Agency (CCTA) to be used by the British government for IT projects. PRINCE 2 and Step Wise are widely used in non-governmental projects also. The file linked below is an example of a project plan for an e-mail system. The plan is developed by following the standards of Step Wise method. This photo shows the steps of Step Wise Planning approach: You can find the example on dropbox by following this link: SPM Project
Read more

Logistic Regression using matlab

Image
Logistic Regression is one of the most famous classification algorithms, like Linear Regression we discussed before , it is easy to implement and gives great predictions. The Logistic Regression algorithm has two major categories: Binary-Class: where the result is 0 or 1 (belong to a certain class or not), an example of it is the spam email detection problem, where the result could be 0 (not spam), or 1 (spam).  Multi-Class: where the result could be any value in the range (0..n), where n is the number of classes, an example of it it the handwritten digit recognition, where we have 10 classes (n = 10), each class represent a number between 0 and 9. Implementing Binary-Class Logistic Regression using matlab is similar to implementing Linear Regression, with some modifications of the hypothesis and cost function formulas. In this video, I implement the algorithm using matlab:
Read more