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GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Autonomous UAV navigation system featuring real-time 2.5D occupancy-grid mapping, A* global path planning, and depth + LiDAR sensor fusion for dynamic obstacle avoidance in complex urban environments. Fully integrated with ROS 2, PX4 Offboard control, and Gazebo simulation for high-fidelity testing and deployment.
This repository contains my hands-on projects and implementations from Udacity's Self-Driving Car Engineer Nanodegree. It covers key concepts in computer vision, deep learning, sensor fusion, path planning, control, and system integration—core skills required to build autonomous vehicle software.