Autonomous driving trajectory planning solution for U-Turn scenario
Structured Prediction Helps 3D Human Motion Modelling - ICCV '19
Working on five computer vision tasks (optical flow, mean-shift tracking, correlation filter tracking, advanced tracking, and long-term tracking) using the programming language Python.
This repository contains solution for SLAM lectures taught by Claus Brenner on YouTube.
Implement SLAM, a robust method for tracking an object over time and mapping out its surrounding environment using elements of probability, motion models, linear algerbra.
A simulation-based robotics localization project implementing an Extended Kalman Filter (EKF) from scratch in Python for 2D robot state estimation. The system fuses noisy IMU and visual odometry measurements in a predict-update framework to improve localization accuracy and reduce drift. Includes realistic sensor noise modelling.
Kidnapped Vehicle (project 6 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
Mobile Robotics Probabilistic Motion Model Tutorial
Extended Kalman Filters
Excercises and examples from the Probabilistic Robotics book by Thrun, Burgard, and Fox.
Unscented Kalman Filters
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