Implementation of Euclidean Clustering for Object Detection Using 3D LiDAR in an Autonomous Vehicle Prototype with Embedded System and ROS

In the pursuit of advancing autonomous driving and automation across various domains, precise obstacle detection stands as an essential feature. Leveraging LiDAR (Light Detection and Ranging) technology, renowned for its ability to provide intricate three-dimensional environmental insights, this article delves into a comprehensive methodology for obstacle detection and tracking. This methodology encompasses key aspects including point cloud preprocessing, segmentation, clustering, and obstacle tracking, all of which collectively contribute to a meticulous and robust perception framework. The article also underscores the merits of deploying a functional prototype and harnessing the potential of the Robot Operating System (ROS) to bolster environmental perception, enabling real-time testing and experimentation. The synthesis of these components not only substantiates the effectiveness of our approach but also highlights its potential implications in enhancing safety and decision-making within autonomous and automated systems.