Roadway Edge Detection Algorithm Based on RANSAC in Underground Mine
Received date: 2019-05-05
Revised date: 2019-09-17
Online published: 2020-02-26
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.
Lin BI , Changming DUAN , Zhuli REN . Roadway Edge Detection Algorithm Based on RANSAC in Underground Mine[J]. Gold Science and Technology, 2020 , 28(1) : 105 -111 . DOI: 10.11872/j.issn.1005-2518.2020.01.043
| 1 | Dragt B J.Modeling and Control of an Autonomous Underground Vehicle[D].Pretoria：University of Pretoria，2006. |
| 2 | 陈盟，王李管，贾明涛，等.地下铲运机自主导航研究现状及发展趋势[J].中国安全科学学报，2013，23（3）：130-134. |
| 2 | Chen Meng，Wang Liguan，Jia Mingtao，et al.An overview of autonomous navigation techniques and development trend for underground LHD[J].China Safety Science Journal，2013，23（3）：130-134. |
| 3 | 姜勇.基于双变量PID控制算法的地下智能铲运机自主导航技术研究[J].机械制造，2016，54（5）：19-22. |
| 3 | Jiang Yong.Technical study on autonomous navigation of smart underground scraper based on bivariate PID control algorithm[J].Machinery，2016，54（5）：19-22. |
| 4 | 杨超，陈树新，刘立，等.反应式导航在地下自主行驶铲运机中的应用[J].煤炭学报，2011，36（11）：1943-1948. |
| 4 | Yang Chao，Chen Shuxin，Liu Li，et al.Reactive navigation for underground autonomous scraper[J].Journal of China Coal Society，2011，36（11）：1943-1948. |
| 5 | Larsson J.Reactive Navigation of an Autonomous Vehicle in Underground Mines[D].Orebro：Orebro University，2007. |
| 6 | Borenstein J，Everett H R，Feng L，et al.Mobile robot positioning：Sensors and techniques[J].Journal of Robotic Systems，1997，14（4）：231-249. |
| 7 | 李晓梅，贾明涛，李宁，等.基于模糊控制的井下自主铲运机的安全导航[J].矿冶工程，2013，33（4）：22-26. |
| 7 | Li Xiaomei，Jia Mingtao，Li Ning，et al.Safe navigation of underground autonomous carry scraper based on fuzzy control[J].Mining and Metallurgical Engineering，2013，33（4）：22-26. |
| 8 | 张绍光.基于局部重建的点云特征点提取[D].大连：大连理工大学，2013. |
| 8 | Zhang Shaoguang.Feature Detection on Point Clouds Via Local Reconstruction[D].Dalian：Dalian University of Technology，2013. |
| 9 | 陈岚峰，杨静瑜，崔崧，等.基于MATLAB的最小二乘曲线拟合仿真研究[J].沈阳师范大学学报（自然科学版），2014，32（1）：75-79. |
| 9 | Chen Lanfeng，Yang Jingyu，Cui Song，et al.MATLAB simulation of curve fitting based on least-squares[J].Journal of Shenyang Normal University （Natural Science Edition），2014，32（1）：75-79. |
| 10 | Hough V，Paul C.Method and means for recognizing complex patterns：U.S.,1771560 [P].1960-03-25. |
| 11 | Fischler M A，Bolles R C.A paradigm for model fitting with applications to image analysis and automated cartography[J].Communications of the Association for Computing Machinery，1981，24（6）：381-395. |
| 12 | Larsson J，Broxvall M，Saffiotti A.Laser based corridor detection for reactive navigation[J].Industrial Robot，2008，35（1）：69-79. |
| 13 | 王旭宸，卢欣辰，张恒胜，等.一种基于平行坐标系的车道线检测算法[J].电子科技大学学报，2018，47（3）：362-367. |
| 13 | Wang Xuchen，Lu Xinchen，Zhang Hengsheng，et al.A lane detection method based on parallel coordinate system[J].Journal of University of Electronic Science and Technology of China，2018，47（3）：362-367. |
| 14 | 杜恩宇，张宁，李艳荻.基于Gabor滤波器的车道线快速检测方法[J].红外与激光工程，2018，47（8）：304-311. |
| 14 | Du Enyu，Zhang Ning，Li Yandi.Lane line quick detection method based on Gabor filter[J].Infrared and Laser Engineering，2018，47（8）：304-311. |
| 15 | 段建民，李岳，庄博阳.基于改进SIS算法和顺序RANSAC的车道线检测方法研究[J].计算机测量与控制，2018，26（8）：280-284，289. |
| 15 | Duan Jianmin，Li Yue，Zhuang Boyang.Lane line detection method research based on improved algorithm of SIS and sequential RANSAC[J].Computer Measurement and Control，2018，26（8）：280-284，289. |
| 16 | 贾会群，魏仲慧，何昕，等.基于神经网络与最小二乘法的车道线检测算法研究[J].汽车工程，2018，40（3）：363-368. |
| 16 | Jia Huiqun，Wei Zhonghui，He Xin，et al.A research on lane marking detection algorithm based on neural network and least squares method[J].Automotive Engineering，2018，40（3）：363-368. |
| 17 | 游俊甫.基于RANSAC的点云数据特征提取[D].南昌：东华理工大学，2015. |
| 17 | You Junpu.Feature Extraction of Point Cloud Data Based on RANSAC[D].Nanchang：East China University of Technology，2015. |
| 18 | Zhang J，Singh S.Low-drift and real-time lidar odometry and mapping[J].Autonomous Robots，2017，41（2）：401-416. |
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