Implementation of Localization using EKF and Particle Filter on a Simple Platform

Based on the University of Washington's robotics course assignments, I have implemented localization using Extended Kalman Filter (EKF) and Particle Filter algorithms on a simple platform. This project serves as a fundamental tutorial for filtering techniques and mobile robot localization, particularly suitable for beginners. The implementation includes key components such as motion models, observation models, and resampling algorithms for particle filters. For deeper understanding, I recommend downloading my previously uploaded Project-1.rar (University of Washington coursework), coding your own solution, and comparing it with my implementation that demonstrates state prediction, measurement update, and covariance management in EKF, along with importance sampling and systematic resampling in particle filters. This comparative approach will help solidify your understanding of probabilistic robotics concepts and enhance your grasp of robot localization principles. Feel free to contact me if you have any technical questions regarding the implementation. Author: Wilford Wang