Physical simulations on Unity

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.

An autonomous robot, powered by AI.

🛸 Navigate urban environments with this autonomous UAV system using real-time occupancy grid mapping and dynamic obstacle avoidance for safe and efficient flights.

Using DepthCleaner of OpenCV-RGB-Depth in python via pybind

The scope of the project covers the creation of 3D grid-based maps with point clouds (Point Cloud) and point clouds provided by depth sensors.

This example shows how to detect objects in an image using Royale's Python wrapper and the pre-trained YOLO network

Extracting the video feed being published by the depth camera by creating an Image Subscriber and displaying the camera feed using OpenCV.

Mapping & localization for B1 indoor navigation (build from zero)