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黄金科学技术 ›› 2020, Vol. 28 ›› Issue (1): 105-111.doi: 10.11872/j.issn.1005-2518.2020.01.043

• 采选技术与矿山管理 • 上一篇    下一篇

基于RANSAC的地下矿山巷道边线检测算法

毕林1,2(),段长铭1,2(),任助理1,2   

  1. 1.中南大学资源与安全工程学院，湖南 长沙 410083
    2.中南大学数字矿山研究中心，湖南 长沙 410083
  • 收稿日期:2019-05-05 修回日期:2019-09-17 出版日期:2020-02-29 发布日期:2020-02-26
  • 通讯作者: 段长铭 E-mail:Mr.BiLin@163.com;Changming_duan@163.com
  • 作者简介:毕林（1975-），男，四川通江人，副教授，从事GIS 、数字矿山和铲运机无人化等方面的研究与软件开发工作。Mr.BiLin@163.com
  • 基金资助:
    国家自然科学基金项目“基于深度学习和距离场的复杂金属矿体三维建模技术研究”(41572317)

Roadway Edge Detection Algorithm Based on RANSAC in Underground Mine

Lin BI1,2(),Changming DUAN1,2(),Zhuli REN1,2   

  1. 1.School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China
    2.Center of Digital Mine Research，Central South University，Changsha 410083，Hunan，China
  • Received:2019-05-05 Revised:2019-09-17 Online:2020-02-29 Published:2020-02-26
  • Contact: Changming DUAN E-mail:Mr.BiLin@163.com;Changming_duan@163.com

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摘要：

巷道边线是井下铲运机反应式导航系统中重要的感知信息。为了准确可靠地在井下环境中感知巷道边线，提出一种基于二维激光扫描信息和随机抽样一致性（RANSAC）的巷道边线检测算法。首先计算每个激光点的曲率，根据曲率阈值将激光点云划分为多个区域；然后基于RANSAC从每个区域提取直线，并根据铲运机航向角及巷道的设计标准进行筛选；最后合并筛选后的激光点云数据，使用RANSAC算法生成最终的巷道边线。基于地下矿山6种典型的巷道场景对算法效果进行验证，结果显示提取的巷道边线可靠度均达到96%以上，且算法具有很高的实时性和稳健性。

关键词: 井下铲运机, 激光雷达, 随机抽样一致性, 巷道边线检测, 反应式导航, 地下矿山

Abstract:

Because the working environment of underground LHD（load-haul-and-dump-machine）is very bad，and with the increase of mining depth in underground mines，the realization of underground unmanned LHD is of great significance for ensuring the safety and health of workers and improving the production efficiency of mining enterprises.Navigation and positioning of LHD is one of the difficulties in the research of unmanned LHD.At present，the navigation technology of underground LHD mainly includes plan-based metric navigation and reactive navigation.The reactive navigation technology has the advantages of low cost and low computation.The former reactive navigation technology mainly relies on adding beacons manually，it has the shortcomings of high cost and poor adaptability.The roadway edge is an important natural beacon perception information，which has natural advantages compared with the artificial beacon.Foreign scholars had applied it to the reactive navigation system of underground LHD and achieved good navigation effect.However，they only did the research on the detection of the roadway edge in the straight roadway，no further discussion on the detection of roadway edge in more complex underground environments.Therefore，a more applicable roadway edge detection algorithm is proposed in this paper.This method is based on two-dimensional laser scanning information and random sampling consistency (RANSAC).The flow chart of the algorithm is as follows：Firstly，the curvature of each laser point in the laser point cloud is calculated，according to the curvature threshold，the laser point cloud data are divided into several regions.RANSAC algorithm is used to extract the roadway edges from each region.Then，the roadway edges are filtered according to the heading angle of the LHD and the design criteria of the roadway.Lastly，the laser point cloud data contained in the remaining roadway edges is merged，and the final roadway edges is generated by RANSAC algorithm again.This article simulated the laser data of six underground mine roadway scenarios，and these six sets of data included typical scenes from simple to complex in underground mine.The experiment was based on MATLAB，and the experimental results were analyzed from the aspects of parallelism，proportion of interior points，fit degree with heading angle，visual display and so on.The calculated results show that the reliability of the extracted roadway edges is more than 96%，and the visual results are in line with the actual situation.This method can detect roadway edge in various scenarios of underground mines，and has high robustness，the roadway edge detection algorithm can play an important technical support role in the reactive navigation of underground LHD，and is of great significance to the realization of the unmanned underground LHD.

Key words: underground LHD, lidar, random sampling consistency, roadway edge detection, reactive navigation, underground mine

中图分类号: 

  • TD525

图1

基于RANSAC的巷道边线检测流程"

图2

激光点云数据集"

图3

巷道边线提取效果图（a）直行巷道分段；（b）转弯处分段；（c）交叉口处分段；（d）直行巷道最终结果；（e）转弯处最终结果；（f）交叉口处最终结果；（g）多交叉口巷道分段；（h）不规则交叉口处分段；（i）采场处分段；（j）多交叉口巷道最终结果；（k）不规则交叉口处最终结果；（l）采场处最终结果"

表1

巷道边线可靠度分析"

数据集序号平行度与航向角契合度内点占比总体可靠度
平均值97.4598.3496.1697.32
（a）99.4399.2394.5097.72
（b）93.0197.78100.0096.93
（c）97.7296.7495.3196.59
（d）97.3399.2294.9097.15
（e）98.7197.7992.2596.25
（f）98.5199.31100.0099.27

表2

算法解算时间"

数据集序号解算时间/ms
平均值72.51
（a）68.46
（b）53.74
（c）76.74
（d）89.47
（e）64.51
（f）82.12

表3

由不同角度分辨率数据提取的巷道边线可靠度"

数据集序号巷道边线可靠度/%
1×角度2×角度3×角度
（a）97.7297.7796.39
（b）96.9392.5093.05
（c）96.5997.3795.74
（d）97.1599.1497.11
（e）96.2595.1796.06
（f）99.2791.2393.85

表4

试验数据方差分析"

方差来源平方和自由度均方F值
总和69.05517
数据角度分辨率11.72325.8621.579
不同井下场景20.19854.0401.088
随机误差37.134103.713
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