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顾及图像增强的煤矿井下视觉同时定位与建图算法

冯玮 姚顽强 蔺小虎 郑俊良 相里海龙 薛志强

冯玮,姚顽强,蔺小虎,等. 顾及图像增强的煤矿井下视觉同时定位与建图算法[J]. 工矿自动化,2023,49(5):74-81.  doi: 10.13272/j.issn.1671-251x.2022090025
引用本文: 冯玮,姚顽强,蔺小虎,等. 顾及图像增强的煤矿井下视觉同时定位与建图算法[J]. 工矿自动化,2023,49(5):74-81.  doi: 10.13272/j.issn.1671-251x.2022090025
FENG Wei, YAO Wanqiang, LIN Xiaohu, et al. Visual simultaneous localization and mapping algorithm of coal mine underground considering image enhancement[J]. Journal of Mine Automation,2023,49(5):74-81.  doi: 10.13272/j.issn.1671-251x.2022090025
Citation: FENG Wei, YAO Wanqiang, LIN Xiaohu, et al. Visual simultaneous localization and mapping algorithm of coal mine underground considering image enhancement[J]. Journal of Mine Automation,2023,49(5):74-81.  doi: 10.13272/j.issn.1671-251x.2022090025

顾及图像增强的煤矿井下视觉同时定位与建图算法

doi: 10.13272/j.issn.1671-251x.2022090025
基金项目: 国家自然科学基金资助项目(42201484)。
详细信息
    作者简介:

    冯玮(1996—),男,河南新乡人,硕士研究生,研究方向为煤矿井下视觉同时定位与建图,E-mail:280155468@qq.com

  • 中图分类号: TD67

Visual simultaneous localization and mapping algorithm of coal mine underground considering image enhancement

  • 摘要: 基于特征点法的视觉同时定位与建图(SLAM)算法在煤矿井下有一定应用,但受光照不均、光照多变、明暗区域交错等因素影响,图像质量较差、纹理信息匮乏,导致视觉SLAM前端特征提取与匹配精度较低,易出现跟踪丢失问题,影响视觉SLAM算法的定位精度与建图效果。提出一种顾及图像增强的煤矿井下视觉SLAM算法,通过图像增强处理提升视觉SLAM的整体性能。采用基于改进双边滤波的Retinex算法对煤矿井下图像进行增强处理:将原始RGB图像转换至HSI色彩空间,以改进的双边滤波代替传统Retinex算法的高斯滤波作为中心环绕函数,对图像反射分量进行估计后转换至RGB色彩空间,得到最终增强图像。将基于改进双边滤波的Retinex算法引入经典ORB−SLAM2算法框架进行位姿估计和建图。基于矿井轮式机器人数据采集平台在煤矿井下巷道环境中对顾及图像增强的视觉SLAM算法进行试验,结果表明:与传统Retinex算法相比,经基于改进双边滤波的Retinex算法增强后的煤矿井下图像未出现明显的泛白及光晕现象,图像质量得到提升;与ORB−SLAM2算法相比,顾及图像增强的视觉SLAM算法提高了特征匹配质量和数量,估计轨迹与真实轨迹的重合度更高,绝对轨迹误差平均值下降了76.2%,且建立的井下巷道三维稠密点云地图更加真实和准确。

     

  • 图  1  顾及图像增强的视觉SLAM算法框架

    ①−基于改进双边滤波的Retinex算法;②−改进双边滤波算法。

    Figure  1.  Visual simultaneous localization and mapping (SLAM) algorithm framework considering image enhancement

    图  2  RGB图像增强流程

    Figure  2.  RGB image enhancement flow

    图  3  传统Retinex算法的图像处理效果

    Figure  3.  Image process effect of traditional Retinex algorithm

    图  4  基于改进双边滤波的Retinex算法流程

    Figure  4.  Retinex algorithm flow based on improved bilateral filter

    图  5  视觉SLAM算法流程

    Figure  5.  Flow of visual SLAM algorithm

    图  6  矿井轮式机器人数据采集平台

    Figure  6.  Data collection platform of wheeled mine-used robot

    图  7  不同算法的图像增强效果

    Figure  7.  Image enhancement effect of different algorithms

    图  8  相邻2帧图像的特征匹配结果

    Figure  8.  Feature matching results of two adjacent image frames

    图  9  SLAM估计轨迹与真实轨迹对比

    Figure  9.  Comparison between estimated trajectories and the real ones of SLAM

    图  10  煤矿井下巷道稠密建图试验结果

    Figure  10.  Densely mapping test results of underground roadway

    表  1  欠曝图像客观评价指标得分

    Table  1.   Objective evaluation index scores of underexposed images

    图像均值标准差平均梯度信息熵
    原始图像44.320 453.125 74.889 54.511 9
    SSR算法处理图像99.605 573.976 97.347 16.609 6
    MSR算法处理图像113.168 177.530 67.551 27.036 1
    改进Retinex算法处理图像122.686 997.890 88.622 97.455 9
    下载: 导出CSV

    表  2  过曝图像客观评价指标得分

    Table  2.   Objective evaluation index scores of overexposed images

    图像均值标准差平均梯度信息熵
    原始图像157.089 687.239 15.399 56.460 7
    SSR算法处理图像163.666 556.840 95.442 05.778 3
    MSR算法处理图像208.409 861.418 45.448 76.211 7
    改进Retinex算法处理图像205.977 489.148 56.043 46.839 6
    下载: 导出CSV

    表  3  正常曝光图像客观评价指标得分

    Table  3.   Objective evaluation index scores of normal exposed images

    图像均值标准差平均梯度信息熵
    原始图像118.563 792.409 06.399 67.015 5
    SSR算法处理图像204.135 995.734 16.984 27.247 6
    MSR算法处理图像193.951 699.818 87.487 77.364 9
    改进Retinex算法处理图像153.404 4100.207 58.604 17.703 7
    下载: 导出CSV

    表  4  煤矿井下图像特征匹配统计结果

    Table  4.   Statistical feature matching results of underground coal mine images

    场景ORB−SLAM2算法本文算法整体提高
    百分比/%
    匹配数量匹配正
    确率/%
    匹配数量匹配正
    确率/%
    欠曝10063.012573.520.9
    过曝10565.512672.715.5
    正常曝光11372.212676.0.8.4
    下载: 导出CSV

    表  5  ATE试验结果

    Table  5.   Test results of absolute trajectory error(ATE) m

    算法最大值最小值平均值RMSE
    ORB−SLAM22.3270.0030.8271.196
    本文算法1.2790.0020.1970.287
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-09-08
  • 修回日期:  2023-05-15
  • 网络出版日期:  2022-11-28

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