基于局部几何−拓扑地图的地下矿自动驾驶定位导航方法

刘仕杰; 邹渊; 张旭东

doi:10.13272/j.issn.1671-251x.2023020010

基于局部几何−拓扑地图的地下矿自动驾驶定位导航方法

北京理工大学机械与车辆学院, 北京　100081

基金项目: 国家自然科学基金项目（52272410）。

详细信息

作者简介:
刘仕杰（1998—），男，辽宁抚顺人，硕士研究生，研究方向为自动驾驶技术，E-mail：986550410@qq.com

通讯作者:
张旭东（1988—），男，江苏徐州人，副教授，博士研究生导师，博士，研究方向为自动驾驶技术，E-mail：xudong.zhang@bit.edu.cn

中图分类号: TD524
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出版历程
- 收稿日期: 2023-02-01
- 修回日期: 2023-08-14
- 网络出版日期: 2023-09-03
- 刊出日期: 2023-08-24

A localization and navigation method for underground mine autonomous driving based on local geometric topology map

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

摘要

摘要: 无人驾驶技术在提高效率、节省成本、减少安全隐患等方面具有巨大优势。针对目前地下环境中定位导航方案实施难度大、成本高、构建地图耗时长等问题，提出了一种基于局部几何−拓扑地图的地下矿自动驾驶定位导航方法。设计了一种局部几何−拓扑地图，井下环境的路网主体结构由拓扑地图表示，该地图上定义了巷道（边）和交叉路口（节点），在每个节点中存储以该节点为中心构建的局部几何地图，用以实现节点处的精确定位。提出了一种基于局部几何−拓扑地图的定位方法，使用基于激光雷达的交叉路口检测算法与交叉路口定位算法进行车辆全局定位。设计了一种基于自适应模型预测控制(MPC)的轨迹跟随算法，保证车辆在交叉路口大曲率转向时的路径跟踪精度。使用三维物理仿真平台构建了地下矿的仿真环境与车辆仿真模型，仿真结果表明：该方法能够实现地下矿自动驾驶定位导航功能，在各种类型交叉路口的定位误差均在0.2 m以内，可以满足自动驾驶的定位精度要求；在整个行驶过程中车辆始终保持较为平稳的行驶状态和较小的跟踪误差。与目前依赖于5G、UWB等技术的定位导航方法相比，该方法仅依赖于激光雷达与惯性测量单元2种车身传感器，在控制设备成本上具有极大优势。
- 地下矿山 /
- 自动驾驶 /
- 定位导航 /
- 局部几何−拓扑地图 /
- 自适应模型预测控制 /
- 轨迹跟随
Abstract: Unmanned driving technology has enormous advantages in improving efficiency, saving costs and reducing safety hazards. In the current implementation of localization and navigation solutions in underground environments, there are problems of implementation difficulties, high costs, and time-consuming construction of maps. In order to solve the above problems, a localization and navigation method for underground mine autonomous driving based on local geometric topology map is proposed. A local geometric topology map has been designed. The main structure of the underground environment road network is represented by a topology map. The map defines roadways (sides) and intersections (nodes), and stores a local geometric map built around the node in each node to achieve precise positioning at the node. A localization method based on local geometric topology map is proposed, which uses a LiDAR-based intersection detection algorithm and intersection localization algorithm for global vehicle localization. A trajectory-following algorithm based on adaptive model predictive control (MPC) has been designed to ensure the path-tracking precision of vehicles turning at high curvature intersections. A simulation environment and vehicle simulation model for underground mines are constructed by using a 3D physical simulation platform. The simulation results show that this method can achieve underground mine autonomous driving localization and navigation functions. The positioning errors are within 0.2 m at various types of intersections, meeting the positioning localization precision requirements of autonomous driving. Throughout the entire driving process, the vehicle maintains a relatively stable driving state and a small tracking error. Compared with the current localization and navigation methods that rely on technologies such as 5G and UWB, this method only relies on two types of vehicle sensors: LiDAR and inertial measurement unit. It has great advantages in controlling equipment costs.
- underground mine /
- autonomous driving /
- localization and navigation /
- local geometric topology map /
- adaptive model predictive control /
- track following

HTML全文

图 1 公制几何地图

Figure 1. Metric geometric map

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图 2 拓扑地图

Figure 2. Topological map

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图 3 局部几何−拓扑地图结构

Figure 3. Structure of local geometric-topological map

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图 4 拓扑地图示例

Figure 4. Example of a topological map

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图 5 激光雷达光束模型

Figure 5. Lidar beam model

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图 6 光束模型距离−角度直方图

Figure 6. Range-angle histogram of the beam model

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图 7 归一化的光束模型距离−角度直方图

Figure 7. Normalized range-angle histogram of the beam model

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图 8 交叉路口检测算法伪代码

Figure 8. Pseudo-code of intersection detection algorithm

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图 9 交叉路口检测效果

Figure 9. Intersection detection effect

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图 10 全局定位算法流程

Figure 10. Flow of global positioning algorithm

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图 11 交叉路口局部定位算法伪代码

Figure 11. Pseudo-code of local positioning algorithm for intersection

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图 12 交叉路口轨迹跟随

Figure 12. Intersection trajectory following

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图 13 巷道行驶

Figure 13. Roadway driving

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图 14 二自由度车辆模型

Figure 14. The two-degree-of-freedom vehicle model

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图 15 地下矿自动驾驶系统框架

Figure 15. Framework of underground mine automatic driving system

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图 16 地下矿环境模型

Figure 16. Model of underground mine environment

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图 17 无人驾驶车辆模型

Figure 17. Model of driverless vehicle

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图 18 地下矿环境拓扑地图

Figure 18. Topological map of underground mine environment

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图 19 车辆行驶路线

Figure 19. Vehicle routes

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图 20 11→10→3路段行驶轨迹

Figure 20. Driving trajectory on section 11→10→3

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图 21 不同算法计算耗时对比

Figure 21. Comparison of calculation time of different algorithms

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图 22 不同算法的点云配准距离差平均值对比

Figure 22. Comparison of average distance differences in point cloud registration of different algorithms

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图 23 定位误差

Figure 23. Positioning error

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图 24 车辆期望前轮转角

Figure 24. Desired front wheel angle of the vehicle

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图 25 实际车辆纵向速度

Figure 25. Actual vehicle longitudinal velocity

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图 26 车辆横摆角及横摆角速度

Figure 26. Vehicle yaw angle and yaw speed

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图 27 车辆横向偏差及横摆角偏差

Figure 27. Vehicle lateral deviation and yaw angle deviation

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图 28 节点2定位结果

Figure 28. Node 2 positioning results

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图 29 节点3定位结果

Figure 29. Node 3 positioning results

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图 30 节点12定位结果

Figure 30. Node 12 positioning results

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