鉱山における自律型インパクトハンマーのためのリアルタイムRGB-D認識パイプライン：自己フィルタリング、岩石セグメンテーション、破砕ポーズ生成
A real-time RGB-D perception pipeline for autonomous impact hammers in mining: self-filtering, rock segmentation and rock-breaking poses generation
鉱山の油圧インパクトハンマー自動化に向け、RGB-Dカメラを用いて岩石をセグメンテーションし、破砕可能なポーズを生成するリアルタイム認識パイプラインを提案した。
著者: Martín Gallegos, Francisco Leiva, Patricio Loncomilla, Michelle Cortés, Javier Ruiz-del-Solar
分類: cs.RO, cs.CV
原文アブストラクト
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.