A real-time RGB-D perception pipeline for autonomous impact hammers in mining: self-filtering, rock segmentation and rock-breaking poses generation
This paper presents a real-time RGB-D perception pipeline for automating hydraulic impact hammers in mining. It combines instance segmentation with point cloud processing, runs at ~10 Hz on embedded hardware with ~675 ms latency, and generates rock-breaking poses and a robot-free 3D workspace representation. Experimental results in a scaled scenario show suitability for real-time autonomous operation.
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[Submitted on 22 Jul 2026]
Title:A real-time RGB-D perception pipeline for autonomous impact hammers in mining: self-filtering, rock segmentation and rock-breaking poses generation
View a PDF of the paper titled A real-time RGB-D perception pipeline for autonomous impact hammers in mining: self-filtering, rock segmentation and rock-breaking poses generation, by Mart\'in Gallegos and 3 other authors
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Abstract:Impact hammers, also known as rock-breakers, are essential machines in mining operations, where they perform secondary reduction. In underground mining, these machines are typically teleoperated, limiting operational efficiency. This paper presents a real-time RGB-D perception pipeline as a step towards automating the operation of hydraulic impact hammers used in mining. The proposed system simultaneously generates operationally feasible rock-breaking poses and a robot-free 3D representation of the workspace. The proposed approach combines image-based instance segmentation with geometric point cloud processing, and operates on embedded hardware at approximately 10 Hz with a total latency of around 675 ms, enabling responsive closed-loop behavior when integrated with a control system. Experimental results in a representative scaled scenario demonstrate that the proposed system is suitable for real-time autonomous impact hammer operation.
Comments: 25 pages, 20 figures
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.20748 [cs.RO]
(or arXiv:2607.20748v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20748
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Francisco Leiva [view email] [v1] Wed, 22 Jul 2026 22:00:29 UTC (39,798 KB)
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