This project utilizes a LIDAR sensor salvaged from a Neato Robot Vacuum Cleaner to perform real-time object detection and tracking. The LIDAR sensor communicates with a Python-based system via UART at a baud rate of 115200, providing continuous 360-degree distance measurements in the environment. Using this data, we convert polar coordinates into a 2D map and differentiate between static objects like walls and dynamic objects like moving people or vehicles.

Our approach uses DBSCAN clustering to group LIDAR data points into meaningful objects, allowing us to visualize and track these entities as they approach or move within the sensor’s field of view. We track the speed and motion of dynamic objects in real-time while marking stationary objects as obstacles. Our system is designed to handle real-world applications such as autonomous driving, robotics, and smart infrastructure.

Further improvements and updates will be shared, as the system evolves to enhance detection accur