Multi sensor adaptive fusion SLAM method for underground mobile robots in coal mines
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摘要: 基于同时定位与建图（SLAM）技术的移动机器人能够快速、准确、自动化地采集空间数据，进行空间智能感知和环境地图构建，是实现煤矿智能化和无人化的关键。针对目前煤矿井下多传感器融合SLAM方法存在机器人前端位姿估计退化失效和后端融合精度不足的问题，提出了一种煤矿井下移动机器人激光雷达（LiDAR）−视觉−惯性（IMU）自适应融合SLAM方法。对LiDAR点云数据进行聚类分割，提取线面特征，利用IMU预积分状态进行畸变校正，采用基于自适应Gamma校正和对比度受限的自适应直方图均衡化（CLAHE）的图像增强算法处理低照度图像，再提取视觉点线特征。用IMU预积分状态为LiDAR特征匹配与视觉特征跟踪提供位姿初始值。根据LiDAR相邻帧的线面特征匹配得到移动机器人位姿，之后进行视觉点线特征跟踪，分别计算LiDAR、视觉、IMU位姿变化值，通过设定动态阈值来检测前端里程计的稳定性，自适应选取最优位姿。对不同传感器构建残差项，包括点云匹配残差、IMU预积分残差、视觉点线残差、边缘化残差。为了兼顾精度与实时性，基于滑动窗口实现激光点云特征、视觉特征、IMU测量的多源数据联合非线性优化，实现煤矿井下连续可用、精确可靠的SLAM。对图像增强前后效果进行试验验证，结果表明，基于自适应Gamma校正和CLAHE的图像增强算法能显著提升背光区和光照区的亮度和对比度，增加图像中的特征信息，大幅提升特征点提取数量和匹配质量，匹配成功率达90.7%。为验证所提方法的性能，在狭长走廊和煤矿巷道场景下进行试验验证，结果表明，所提方法在狭长走廊场景的定位均方根误差为0.15 m，构建的点云地图一致性较高；在煤矿巷道场景中的定位均方根误差为0.19 m，构建的点云地图可真实地反映煤矿井下环境。Abstract: Mobile robots based on simultaneous localization and mapping (SLAM) technology can quickly, accurately, and automatically collect spatial data for spatial intelligent perception and environmental map construction. It is the key to achieving intelligent and unmanned coal mines. However, the current multi sensor fusion SLAM method in coal mines suffers from degradation and failure in robot front-end pose estimation, as well as insufficient precision in back-end fusion. This study proposes a LiDAR-visual-IMU adaptive fusion SLAM method for underground mobile robots in coal mines. The method clusters and segments LiDAR point cloud data, extracts line and surface features, and uses IMU pre integration state for distortion correction. The method uses image enhancement algorithm based on adaptive Gamma correction and contrast limited adaptive histogram equalization (CLAHE) to process low light images, and then extracts visual point and line features. The method provides initial pose values for LiDAR feature matching and visual feature tracking using IMU pre integration state. The pose of the mobile robot is obtained by matching the line and surface features of adjacent frames of LiDAR. Then, visual point and line feature tracking is performed to calculate the LiDAR, visual, and IMU pose changes. The stability of the front-end odometer is detected by setting dynamic thresholds, and the optimal pose is adaptively selected. The method constructs residuals for different sensors, including point cloud matching residuals, IMU pre integration residuals, visual point line residuals, and edge residuals. In order to balance precision and real-time performance, a sliding window based joint nonlinear optimization of multi-source data for laser point cloud features, visual features, and IMU measurements is implemented to achieve continuous and reliable SLAM in coal mines. Experimental verification is conducted on the effects before and after image enhancement. The results show that the image enhancement algorithm based on adaptive Gamma correction and CLAHE can significantly improve the brightness and contrast of the backlight and lighting areas, increase the feature information in the image, and significantly improve the quality of feature point extraction and matching. It achieves a matching success rate of 90.7%. To verify the performance of the proposed method, experimental verification is conducted in narrow corridor and coal mine roadway scenarios. The results show that the root mean square error of the proposed method in narrow corridor scenarios is 0.15 m, and the consistency of the constructed point cloud map is high. The root mean square error of positioning in the coal mine roadway scenario is 0.19 m. The constructed point cloud map can truly reflect the underground environment of the coal mine.
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图 1 LiDAR−视觉−惯性融合SLAM方法原理
Figure 1. Principle of LiDAR-visual -inertial measurement unit (IMU) fusion simultaneous localization and mapping (SLAM) method
图 5 狭长走廊与煤矿巷道环境及控制点布设情况
Figure 5. Control points layout in narrow corridor and coal mine roadway environment
表 1 多源传感器设备型号与信息
Table 1. Sensors model and information
设备 设备型号 设备信息 LiDAR VLP−16 频率：10 Hz ；最大测量距离：150 m IMU Ellipse2−N 频率：200 Hz ；横滚/俯仰误差为±0.1°；航向误差为0.5° 相机 Zed−2i 频率：30 Hz； 图像分辨率（H×V）：1 280×720 控制台 Autolabor−PC CPU：AMD Ryzen3 3 200G、DDR4 8 G 机器人 Autolabor−Pro1 驱动模式：四驱；位移速度：0.5～1.5 m/s；旋转角速度：0.56 rad/s 
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表 2 特征提取与跟踪对比
Table 2. Features extraction and tracking comparison
算法 特征点数量/个 匹配数量/个 匹配成功率% M7 M9 M7 M9 M7 M9 原图 273 308 89 122 32.6 39.6 Retinex算法 368 390 242 269 65.8 69.0 自适应Gamma校正算法 330 367 250 286 75.8 77.9 CLAHE算法 329 356 276 303 83.9 85.1 文献[30]算法 347 374 297 331 85.6 88.5 本文算法 365 392 331 365 90.7 93.1
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表 3 定位轨迹的标准差和均方根误差
Table 3. Root mean squared error and standard deviation of positioning trajectory
m 误差 狭长走廊 煤矿巷道 本文方法 IE−LVI−SAM LIO−Mapping IE−ORB−SLAM2 ORB−SLAM2 本文方法 IE−LVI−SAM LIO−Mapping IE−ORB−SLAM2 ORB−SLAM2 STD 0.06 0.27 0.78 0.89 3.39 0.08 0.10 0.24 0.57 4.05 RMSE 0.15 0.39 1.25 2.58 8.12 0.19 0.28 0.75 0.60 6.82
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