Research status and development trends of SLAM technology in autonomous mining field
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摘要: 无人驾驶是矿山智能化关键技术之一，其中即时定位与地图构建（SLAM）技术是实现无人驾驶的关键环节。为推动SLAM技术在矿山无人驾驶领域的发展，对SLAM技术原理、成熟地面SLAM方案、现阶段矿山SLAM研究现状、未来矿山SLAM发展趋势进行了探讨。根据SLAM技术所使用的传感器，从视觉、激光及多传感器融合3个方面分析了各自的技术原理及相应框架，指出视觉和激光SLAM技术通过单一相机或激光雷达实现，存在易受环境干扰、无法适应复杂环境等缺点，多传感器融合SLAM是目前最佳的解决方法。探究了目前矿山SLAM技术的研究现状，分析了视觉、激光、多传感器融合3种SLAM技术在井工煤矿、露天矿山的适用性与研究价值，指出多传感器融合SLAM是井工煤矿领域的最佳方案，SLAM技术在露天矿山领域研究价值不高。基于现阶段井下SLAM技术存在的难点（随时间及活动范围积累误差、各类场景引起的不良影响、各类传感器无法满足高精度SLAM算法的硬件要求），提出矿山无人驾驶领域SLAM技术未来应向多传感器融合、固态化、智能化方向发展。Abstract: Autonomous driving is identified as one of the key technologies for mining intelligence, with simultaneous localization and mapping (SLAM) technology serving as a key link to realize autonomous driving. To advance the development of SLAM technology in autonomous mining, this paper discusses the principles of SLAM technology, mature ground SLAM solutions, the current research status of mining SLAM, and future development trends. Based on the sensors employed in SLAM technology, the study analyzes the technical principles and corresponding frameworks from three aspects: vision, laser, and multi-sensor fusion. It is noted that visual and laser SLAM technologies, which utilize single cameras or LiDAR, are susceptible to environmental interference and cannot adapt to complex environments. Multi-sensor fusion SLAM emerges as the most effective solution. The research examines the status of mining SLAM technology, analyzing the applicability and research value of visual, laser, and multi-sensor fusion SLAM technologies in underground coal mines and open-pit mines. It concludes that multi-sensor fusion SLAM represents the optimal research approach for underground coal mines, while the research value of SLAM technology in open-pit mines is limited. Based on the challenges identified in underground SLAM technology, such as accumulated errors over time and activity range, adverse effects from various scenes, and the inadequacy of various sensors to meet the hardware requirements for high-precision SLAM algorithms, it is proposed that future developments in SLAM technology for autonomous mining should focus on multi-sensor fusion, solid-state solutions, and intelligent development.
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图 3 ORB−SLAM框架
Figure 3. Oriented features from accelerated segment test and rotated binary robust independent elementary features simultaneous localization and mapping（ORB-SLAM） framework
图 4 LSD−SLAM框架
Figure 4. Large-scale direct monocular simultaneous localization and mapping （LSD-SLAM ）framework
图 7 LOAM−Livox框架
Figure 7. LiDAR odometry and mapping for Livox （LOAM-Livox） framework
图 8 VINS−Mono框架
Figure 8. Visual inertial navigation system-monocular（VINS-Mono） framework
图 9 LIO−SAM框架
Figure 9. Lidar-inertial odometry via smoothing and mapping （LIO-SAM） framework
图 10 LVI−SAM框架
Figure 10. Lidar-visual-inertial odometry via smoothing and mapping（LVI-SAM） framework
图 19 井下相机−激光雷达−IMU融合SLAM框架
Figure 19. Underground Camera-LiDAR-IMU fusion SLAM framework
表 1 上文未提及的常见多传感器融合SLAM方案
Table 1. Common multi-sensor fusion SLAM solutions
多传感器融合SLAM方案 所属类型 优点 缺点 激光−惯性里程计与地图构建[31] 激光雷达−IMU 率先开源的激光雷达与IMU融合方案 计算效率不高 激光−惯性状态估计器[32] 激光雷达−IMU 相较LIO−mapping运行速度提高近1个数量级 复杂程度高 快速激光−惯性里程计系列[33-35] 激光雷达−IMU 轻量级定位建图，运行效率高；Faster−LIO
可应用至固态激光雷达牺牲了一定精度；更适合小尺度场景 固态激光雷达−惯性里程计与地图构建[36] 激光雷达−IMU 适用于固态激光雷达的融合方案 狭窄长廊特征匹配退化严重 激光−惯性里程计与地图构建[37] 激光雷达−IMU 可消除动态物体影响；低漂移；强鲁棒性 实时性较差 基于关键帧的视觉−惯性SLAM系统[38] 相机−IMU 轨迹估计精确 无回环检测；无法构建环境地图 基于方向加速分割测试特征检测子和
旋转二进制鲁棒独立特征描述子的
视觉−惯性SLAM系统[39]相机−IMU 通过融合IMU数据解决快速运动下
特征点丢失的问题无法长时间应用至光照变化明显、
欠特征场景基于方向加速分割测试特征检测子和
旋转二进制鲁棒独立特征描述子的
即时定位与地图构建[40]相机−IMU 定位精度、实时性好 快速运动场景存在特征丢失问题；
大尺度场景计算消耗量大视觉−激光里程计与地图构建[41] 激光雷达−相机−IMU 精度高；鲁棒性好 无“回环检测” 激光−单目视觉里程计[42] 激光雷达−相机−IMU 环境信息丰富，便于后续语义分割等操作 精度低于V−LOAM 稳健实时的激光−惯性−视觉联合估计[43-45] 激光雷达−相机−IMU 实时性强；可拓展性优秀 计算资源需求大 快速激光−惯性−视觉里程计[46] 激光雷达−相机−IMU 计算效率高；鲁棒性好 硬件要求高 
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