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.
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.
Excercises and examples from the Probabilistic Robotics book by Thrun, Burgard, and Fox.
Mobile Robotics Probabilistic Motion Model Tutorial
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