Robust Rock Detection and Clustering with Surface Analysis for Robotic Rock Breaking Systems

A reliable and robust visual perception system is often a key-enabler for autonomous robotic systems working in at least partially unknown environment. For autonomous robotic rock breaking system, boulder detection and localization system is an essential part that must be able to operate under challenging environments. The problem can be summarized as detecting unstructured objects in a structured environment. A key to robust boulder detection is an effective clustering algorithm, that can segment a point cloud, captured by e.g., a Time-of-Flight (TOF) camera, into clusters of individual boulders. This information is crucial for an autonomous robotic secondary breaking system, that uses it to break each individual boulder by a hydraulic impact hammer. This study proposes a novel algorithm for point cloud segmentation that can be used for the boulder detection application. The method is designed using features of the Point Cloud Library (PCL), and it is benchmarked against other readily available algorithms in the PCL. The results indicate robust performance with an impressive 97.4% accuracy on our dataset.