Developed a ROS2 autonomous navigation pipeline using Python, OpenCV, PyTorch, and LEDNet for real-time trail segmentation, combining YOLOv7, lidar, and camera fusion for pedestrian-aware robotic navigation.
Engineered a 3D vision pipeline using Python, OpenCV, and YOLOv3 on KITTI stereo images, fusing depth maps with 2D detections to segment vehicles and estimate positions and distances.
Built a Python pipeline using OpenCV, HSV segmentation, and K Means clustering to detect targets from drone imagery and transform them into global coordinates via calibrated camera models.
Created a 3D point cloud alignment system in Python using the ICP algorithm, nearest-neighbour search, and SVD to estimate optimal transformations between source and target scans.
Designed a MATLAB/Simulink quadrotor control system using state-space modeling, kinematics, and cascaded PID control to simulate UAV flight dynamics, stability, and path tracking performance.
Developed autonomous drone racing capabilities using Python, RRT*, and PyBullet, combining trajectory optimization, polynomial path fitting, and sim-to-real deployment on a Crazyflie quadrotor.
Developed a Python visual odometry system using OpenCV and NumPy to estimate vehicle pose and localization from KITTI stereo images, leveraging RANSAC, point cloud alignment, and SVD.
Implemented an Extended Kalman Filter for 2D robot localization, fusing wheel odometry and laser range data to estimate pose under noisy, partially observable environments with uncertain measurements.
Led software development for a ROS-based autonomous robot using Python, C++, LiDAR, Hector SLAM, and AMCL for mapping, localization, and navigation with real-time obstacle avoidance.
Developed autonomous navigation algorithms for Turtlebot using ROS, Python, C++, and Gazebo, combining heuristic planning and sensor feedback to optimize exploration paths in unknown environments.
Implemented optimal trajectory planning for a 2-DOF robotic manipulator using CasADi and IPopt, applying direct optimal control to optimize motion for energy, time, and torque objectives.
Compared sampling-based motion planners in Python by applying RRT and RRT* to a Dubins vehicle, demonstrating improved path optimality through rewiring while respecting vehicle constraints.