A Case Study in Generating 3D Maps with Collaborative Robots

This research presents a case study on the creation of detailed 3D maps using the Agilex Scout Mini robot equipped with a 3D LiDAR, aimed at improving autonomous navigation in indoor environments. ROS (Robot Operating System) is employed for real-time data acquisition and processing, and the DBSCAN clustering algorithm for segmenting point cloud data. These maps were subsequently integrated into the humanoid robot Pepper, developed by Aldebaran Robotics, enabling Pepper to navigate autonomously and deliver location-based services at the West University of Timisoara. Our results show a point cloud density of approximately 5,000 points per cubic meter, highlighting the LiDAR sensor's capability to capture fine environmental details. Efficient clustering was achieved with an average computation time of 3 seconds per chunk, and path planning demonstrated a high smoothness score of 0.8, improving navigation efficiency. These findings contribute to the field by demonstrating the effectiveness of combining 3D mapping with autonomous robotics for reliable, real-time navigation. Moreover, the integration of Intel RealSense cameras is proposed as a future enhancement to improve object recognition and colour mapping, further expanding the system's potential applications. This case study not only advances academic understanding of autonomous robotic systems but also presents practical and scalable solutions for real-world navigation challenges, offering significant improvements in both accuracy and efficiency.