Bimodal environment perception technology for underground coal mine based on radar and visual fusion
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摘要: 环境感知是煤矿巡检机器人、视觉测量系统等场景应用的关键技术。单模态环境感知技术对煤矿井下复杂环境的感知能力较差。提出了雷达与视觉双模态空间融合方法，通过激光雷达和摄像仪之间的坐标转换来实现二者采集信息的融合，从而提高环境感知能力。为了更好地提取目标特征信息，提出了双模态融合环境感知网络架构技术路线：摄像仪和雷达采集的环境信息经雷达与视觉双模态空间融合方法进行融合处理，多模态特征融合网络模块提取融合信息中的目标特征，多任务处理网络模块采用不同的任务头处理目标特征信息，完成目标检测、图像分割、目标分类等环境感知任务。采用YOLOv5s目标检测算法搭建双模态特征提取网络模块进行实验，结果表明：基于雷达与视觉融合的双模态煤矿井下环境感知技术对井下巷道环境下行人检测的成功率较视觉、雷达感知分别提升15%，10%，对车道线、标志牌等各类目标分割的平均精度均值较视觉感知均提高10%以上，有效提升了煤矿井下环境感知能力，可为煤矿道路环境感知、视觉测量系统、无人矿车导航系统、矿井搜救机器人等应用场景提供技术支持。Abstract: Environmental perception is a key technology for scenario applications such as coal mine inspection robots and visual measurement systems. The single modal environmental perception technology has poor perception capability for complex environments in underground coal mines. A bimodal space fusion method for radar and vision has been proposed. The modal achieves the fusion of information collected by LiDAR and camera through coordinate conversion, thereby improving environmental perception capability. In order to better extract object feature information, a bimodal fusion environment perception network architecture technology route is proposed. The environmental information collected by the camera and radar is fused and processed by the radar and visual bimodal space fusion method. The multimodal feature fusion network module extracts object features from the fused information. The multitask processing network module uses different task heads to process object feature information, completing environmental perception tasks such as object detection, image segmentation, and object classification. The experiment is conducted using the YOLOv5s object detection algorithm to build a bimodal feature extraction network module. The results show that the success rate of the bimodal environment perception technology for underground coal mine based on radar and visual fusion for personnel detection in underground roadway environments is improved by 15% and 10% compared to visual and radar perception, respectively. The mean average precision of segmentation for various types of objects such as lane lines and signs are improved by more than 10% compared to visual perception. It effectively improves the perception capability of underground environment in coal mines, providing technical support for application scenarios such as coal mine road environment perception, visual measurement systems, unmanned mining vehicle navigation systems, and mine search and rescue robots.
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图 2 雷达坐标系与世界坐标系的转换关系
Figure 2. Conversion relationship between radar coordinate system and world coordinate system
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图 5 双模态融合环境感知技术网络架构
Figure 5. Network architecture of bimodal fusion environment perception technology
CPU i9−9820X GPU NVIDIA RTX 2080 Ti GPU×2 操作系统 Ubuntu 20.04 架构 PyTorch 实验总轮数 100 批数大小 4 初始学习率 0.001 模型优化器 AdamW 权重衰减项 1×10−4 
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表 1 模型轻量化效果
Table 1 Model lightweighting effects
模型 模型大小/MiB 平均精度/% 平均帧率/
（帧·s−1）每秒浮点运算
次数/106轻量化前 256 87.7 28.7 53.17 轻量化后 52.6 82.6 49.8 30.23
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表 2 单模态与双模态检测结果对比
Table 2 Comparison between of unimodal and bimodal detection results
感知方式 目标总数/个 检测数/个 漏检数/个 成功率/% 视觉 60 45 15 75 雷达 60 48 12 80 融合 60 54 6 90
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表 3 煤矿井下各类目标分割结果的评价指标
Table 3 Evaluation indexes of segmentation results of various types of object in underground coal mine
% 目标 纯图像 双模态空间融合图像 mIoU 召回率 mAP mIoU 召回率 mAP 背景 83.21 75.74 99.22 85.62 89.61 99.78 车辆 77.48 81.62 79.82 79.40 84.50 88.92 路面 79.27 79.80 72.31 82.12 91.30 82.38 车道线 74.21 67.90 68.35 80.21 91.56 77.62 标志牌 74.01 78.33 60.54 78.88 90.37 79.85 行人 84.21 83.44 66.95 90.25 95.65 83.44
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