面向无人驾驶矿车的露天矿山道路坡度实时检测方法  

Road slope real-time detection for unmanned truck in surface mine

作　　者：孟德将 田滨[2,3] 蔡峰 高义军[5] 陈龙 MENG Dejiang;TIAN Bin;CAI Feng;GAO Yijun;CHEN Long(Beijing Waytous Technologies Co., Ltd., Beijing 100190, China;State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China;China National Coal Group Corp., Beijing 100120, China;Magang Group Nanshan Mine Co., Ltd., Ma’anshan 243031, China;School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China)

机构地区：[1]北京慧拓无限科技有限公司,北京100190 [2]中国科学院自动化研究所复杂系统管理与控制国家重点实验室,北京100190 [3]中国科学院大学人工智能学院,北京100190 [4]中国中煤能源集团有限公司,北京100120 [5]安徽马钢矿业资源集团南山矿业有限公司,马鞍山243031 [6]中山大学数据科学与计算机学院,广州510006

出　　处：《测绘学报》2021年第11期1628-1638,共11页Acta Geodaetica et Cartographica Sinica

基　　金：广东省重点领域研发计划(2020B0909050001)。

摘　　要：露天矿山大部分道路坡度大,无人驾驶矿车在上下坡之前如果不能合理规划速度,则容易发生一些危险,例如因下坡急减速导致的物料外撒或因上坡导致的溜车。无人驾驶矿车通过精确检测车辆前方一定范围内的道路坡度,可以在上下坡之前合理规划速度。目前无人驾驶矿车实时检测露天矿山道路坡度存在一些挑战:露天矿山道路坡度大且不平整,无人驾驶矿车在行驶过程中会有比较大的俯仰和弹跳运动,因为基于惯性导航系统(inertial navigation system,INS)或全球导航卫星系统(Global Navigation Satellite System,GNSS)的方法测出的是车辆的俯仰角,而不是所在道路区域的坡度;由于露天矿山几何特征缺失,这使得基于SLAM(simultaneous localization and mapping)的方法在特征缺失的路段容易匹配错误,而且基于SLAM的方法测出的也是车辆的俯仰角。本文针对目前无人驾驶矿车实时检测露天矿山道路坡度研究中存在的问题,提出了栅格卡尔曼道路坡度实时检测(grid Kalman road slope real-time detection,GKSRD)方法。该方法以三维激光雷达点云和INS俯仰角信息作为输入,并采用二维栅格地图、感兴趣矩形区域迭代优化算法和卡尔曼滤波器。相比于基于INS或GNSS的方法,该方法减小了无人驾驶矿车行驶过程中由于道路坡度大且不平整对道路坡度实时检测带来的误差。相比于基于SLAM的方法,因为该方法不依赖周围环境的几何特征,所以其不会受到露天矿山几何特征缺失的影响。通过试验验证,GKSRD方法对露天矿山道路坡度的实时检测平均误差小于0.01°,最大误差小于0.5°。相比于基于INS或GNSS的方法和基于SLAM的方法,GKSRD方法精度更高,稳定性和环境适应性也更好。Given the steepness of road slope in surface mines,one unmanned truck may run at risk in unknown environment if it can’t plan a proper speed in advance.Therefore,it is crucial for an autonomous vehicle to perceive an accurate value of road slope of it in real time.However,the accuracy is hard to achieve in existing methods,including global navigation satellite system(GNSS),inertial navigation system(INS)and simultaneous localization and mapping(SLAM).For GNSS or INS,they can measure a truck’s angle of pitch,but this angle cannot be equal to road slope due to its large pitching and bouncing movements caused by steep and uneven roads.For the same reason,SLAM doesn’t work well either.Also,it will lose efficacy as geometric features are not obvious in open-pit mine.To deal with these challenges,this paper proposes a grid Kalman road slope real-time detection(GKSRD)method.The method’s input is 3D point cloud of lidar and the pitch of INS.And the method uses a 2D grid map,an iterative optimization algorithm in rectangular region of interest and the Kalman filter.Compared with methods based on GNSS or INS,this method minimizes the error of slope detection.Different from methods based on SLAM,this method does not rely on geometric features of the environment.Verified by experiments,the average error of the road slope detected by GKSRD method is less than 0.01 degree,and the maximum error is less than 0.5 degree.Hence,compared with methods based on INS or GNSS and methods based on SLAM,GKSRD method is more accurate,stable and adaptive.

关 键 词：无人驾驶矿车 道路坡度实时检测 三维激光雷达 露天矿山 

分 类 号：P227[天文地球—大地测量学与测量工程]