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.
Kidnapped Vehicle (project 6 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
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.
Extended Kalman Filters
Excercises and examples from the Probabilistic Robotics book by Thrun, Burgard, and Fox.
Mobile Robotics Probabilistic Motion Model Tutorial
Unscented Kalman Filters
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