煤矿移动机器人LiDAR/IMU紧耦合SLAM方法  

LiDAR/IMU tightly-coupled SLAM method for coal mine mobile robot

作　　者：李猛钢 胡而已 朱华[1] LI Menggang;HU Eryi;ZHU Hua(School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China;Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment,China University of Mining and Technology,Xuzhou 221008,China;State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Information Institute,Ministry of Emergency Management of the People's Republic of China,Beijing 100029,China)

机构地区：[1]中国矿业大学机电工程学院,江苏徐州221116 [2]江苏省矿山智能采掘装备协同创新中心,江苏徐州221008 [3]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [4]应急管理部信息研究院,北京100029

出　　处：《工矿自动化》2022年第12期68-78,共11页Journal Of Mine Automation

基　　金：国家自然科学基金资助项目(52274159);江苏省自然科学基金资助项目(BK20210497);机器人学国家重点实验室联合开放基金项目(2022-KF-22-05)。

摘　　要：煤矿井下机器人同步定位与地图构建(SLAM)是当前研究热点,但针对提高激光SLAM在井下复杂条件下精度、鲁棒性的研究仍然不足;传统激光SLAM方法在井下复杂环境下存在累计误差迅速增大、旋转过程鲁棒性差、特征关联错误率高等问题;现有激光−惯性融合的定位建图紧耦合融合机制仍需进一步提高对煤矿井下复杂环境的适应能力。针对上述问题,提出了一种煤矿机器人LiDAR(激光雷达)/IMU(惯性测量单元)紧耦合SLAM方法(LI−SLAM方法)。首先利用IMU观测信息预测点云运动状态并进行有效补偿,减少由于剧烈振动、快速旋转等恶劣运动工况导致的点云畸变;然后提取雷达点云的边线与平面特征,基于点−线和点−面扫描匹配构建激光相对位姿约束,并在向量空间与流形空间解析推导了约束的残差、雅可比矩阵、协方差矩阵构建过程;最后通过构建雷达相对位姿约束因子、IMU预积分约束因子、回环检测约束因子,基于因子图优化方法完成LiDAR/IMU紧耦合,实现井下复杂环境下煤矿移动机器人的定位与地图构建。为了验证LI−SLAM方法在颠簸路面、复杂场景的精度与鲁棒性,基于煤矿轮式移动机器人平台,在野外、地下车库环境下进行了试验,在晋能集团塔山煤矿开展了工业性试验,并与当前最优的激光里程计与建图(LOAM)方法、激光雷达惯性状态估计(LINS)方法、雷达惯性里程计与建图(LIO−mapping)方法进行了对比。在野外颠簸路面的试验结果表明:LI−SLAM方法和LOAM方法的地图一致性最好,与真实路线基本吻合,LI−SLAM方法对旋转有更佳的适应能力,距离误差最小;LIO−mapping方法无法实时运行,在0.5倍速下可以获得完整轨迹,但在初始运动阶段出现了较大程度的方向偏移,初始化过程容易失败;LINS方法由于仅利用了最新的观测信息,在复杂地形下出现了漂移。地下车库环境下的试�SLAM(Simultaneous Localization and Mapping)of the underground robot is a hot research topic at present.But the research on improving the precision and robustness of laser SLAM in underground complicated conditions is still insufficient.The traditional laser SLAM method has the problems of rapidly increasing cumulative error,poor robustness of the rotation process and high error rate of feature correlation under complex underground environment.The existing laser inertial fusion location mapping tightly-coupled fusion mechanism still needs to further improve the adaptability to the complex environment in coal mines.In order to solve the above problems,a LiDAR(lidar)/IMU(inertial measurement unit)tightly-coupled SLAM(LI-SLAM)method for coal mine robot is proposed.Firstly,the IMU observation information is used to predict the point cloud motion state and make effective compensation to reduce the point cloud distortion caused by severe vibration,rapid rotation and other severe motion conditions.Secondly,the edge and plane features of the radar point cloud are extracted.The laser relative pose constraints are constructed based on point line and point surface scanning matching.In vector space and manifold space,the construction process of residual,Jacobian matrix and covariance matrix of constraints is derived analytically.Finally,the LiDAR/IMU tight coupling is completed based on the factor graph optimization method by constructing the radar relative pose constraint factor,IMU preintegration constraint factor and loopback detection constraint factor.The localization and map construction of the mine mobile robot in the complex underground environment is realized.In order to verify the precision and robustness of the LI-SLAM method in the bumpy road and complex scenario,experiments are carried out in the field and underground garage environment based on the platform of wheeled mobile robot in the coal mine.The industrial experiments are carried out in Tashan Coal Mine of Jinneng Group.The results are compared with the cu

关 键 词：煤矿移动机器人 同步定位与地图构建 雷达−惯性融合 因子图优化 紧耦合 

分 类 号：TD67[矿业工程—矿山机电]