DBSCAN and Yolov5 based 3D object detection and its adaptation to a mobile platform

Abstract

This study presents a 3D object detection technology for mobile platforms and its application. Rather than an innovative high-performance model, we proposed a “useable” model for the robot industry at the current technology stage by combining various techniques. To reduce computation time, a 2D region proposal was obtained using a RGB image-based CNN model. By applying the DBSCAN clustering technique to the point cloud corresponding to the 2D region proposal, a method of obtaining a 3D region proposal was proposed. This allowed for 3D object detection using an RGB image dataset, which has been widely researched, while reducing the computation load to a level suitable for use in mobile robots. Furthermore, the 3D object detection was integrated into a ROS 2-based mobile platform, which was used to perform pedestrian-safe avoidance tasks and elevator button operation tasks. The performance was confirmed through experiments.