面向地下采矿环境中异构机器人协作的空间感知
Towards Spatial Perception for Heterogeneous Robot Collaboration in Subterranean Mining Environments
浏览论文内容
中文总结 AI 辅助
本文提出一种连接异构机器人的机载感知流水线，通过零样本视觉语言分割生成场景图，并利用几何抽象将检测转化为可操作目标，实现地下矿山自主勘探与检查。
中文摘要 AI 辅助
废弃地下矿井中深层矿藏的自主开采本质上是一个多智能体集成问题。没有任何单一平台能够同时提供穿越数公里退化巷道所需的机动性和表征矿体所需的传感载荷。本文介绍了在PERSEPHONE自主采矿任务中连接两个异构智能体的机载感知流水线。该流水线由一个轻量级Explorer机器人组成，该机器人通过运行零样本、视觉-语言语义分割栈，直接从自然语言提示中检测矿藏，从而绘制未知矿井地图并生成检查目标的三维场景图。随后，地图和图被传递给第二个Inspector机器人，该机器人携带先进的传感载荷，并利用它们规划近距离检查视点。我们详细介绍了完整的流水线，重点强调了将原始检测转化为可操作检查目标的几何抽象，涵盖逐视图边界框生成、跨视图框合并、平面拟合和多边形提取，并报告了在地下测试设施和一座活跃的菱镁矿中的广泛现场验证，涵盖了在现实、感知退化条件下铁脉和菱镁矿矿化的情况。
英文摘要
The autonomous extraction of deep mineral deposits in abandoned underground mines is fundamentally a multi-agent integration problem. No single platform simultaneously offers the mobility to traverse kilometers of degraded drifts and the sensing payload required to characterize an ore body. This article presents the onboard perception pipeline that bridges two heterogeneous agents within the PERSEPHONE autonomous mining mission. Which consist of a lightweight Explorer robot that maps an unknown mine and generates a 3D scene graph of inspection targets, by running a zero-shot, vision-language semantic segmentation stack that detects mineral deposits directly from natural-language prompts. The map and the graph are then handed to a second Inspector robot, which carries an advanced sensing payload and uses them to plan close-range inspection viewpoints. We detail the complete pipeline, with emphasis on the geometric abstraction that turns raw detections into actionable inspection targets, spanning per-view bounding-box generation, cross-view box merging, plane fitting, and polygon extraction, and we report an extensive field validation in a subterranean test facility and in an active magnesite mine, covering both iron-vein and magnesite mineralization under realistic, perceptually degraded conditions.
发表机构
- Luleå University of Technology（吕勒奥理工大学）
机构由 AI 辅助整理，请以论文原文为准。
