巡检机器人对室内场景进行自主导航监测时,采用视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)方法构建的三维深度地图存在实时性不高、定位精度下降的问题。对此,提出了一种基于RGB-D相机和优化RTAB-Map(real ti...巡检机器人对室内场景进行自主导航监测时,采用视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)方法构建的三维深度地图存在实时性不高、定位精度下降的问题。对此,提出了一种基于RGB-D相机和优化RTAB-Map(real time appearance based mapping)算法的巡检机器人视觉导航方法。首先,通过重新配置RTAB-Map点云更新频率,实现算法优化,构建稠密的点云地图后;采用启发式A*算法、动态窗口法(dynamic window approach,DWA)分别制定全局与局部巡检路径,通过自适应蒙特卡罗定位(adaptive Monte Carlo localization,AMCL)方法更新机器人的实时位姿信息,再将搭建好的实体巡检机器人在软件、硬件平台上完成视觉导航测试实验。结果表明:优化后的RTAB-Map算法运行时的内存占比稍有增加,但获得与真实环境一致性更高的三维深度地图,在一定程度上提高视觉导航的准确性与实用性。展开更多
在基于视觉的即时定位与建图(Simultaneous Localization and Mapping,SLAM)中,RTAB-Map是一个比较经典的解决方案,它包含有鲁棒的视觉里程计,同时也提供稠密点云地图、2D占据栅格地图和Octomap(3D占据栅格地图)三种地图构建形式。但稠...在基于视觉的即时定位与建图(Simultaneous Localization and Mapping,SLAM)中,RTAB-Map是一个比较经典的解决方案,它包含有鲁棒的视觉里程计,同时也提供稠密点云地图、2D占据栅格地图和Octomap(3D占据栅格地图)三种地图构建形式。但稠密点云地图数据量大,无法适用于机器人导航;2D占据栅格地图虽数据量小,但无法反映复杂地形特征,一般只用于室内扫地机器人导航;Octomap能较好地反映三维空间内障碍物的信息,多用于无人机的导航,但对于地面移动机器人来说存在信息冗余。为RTAB-Map扩展了2.5D高程栅格地图构建模块,这种地图可以很好地反映地形环境特征,且地图所占用存储空间更小,更能充分利用移动机器人有限的存储和计算资源。展开更多
Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely.However,this task inherently exposes monitoring staff to hazardous environments,especially in und...Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely.However,this task inherently exposes monitoring staff to hazardous environments,especially in underground settings.Since 2000s,robots have been widely applied in various fields and many studies have focused on establishing autonomous mobile robotic systems as well as solving the issue of underground navigation and mapping.However,only a few studies have conducted quantitative evaluations of these methods,and almost none have provided a systematic and comprehensive assessment of the suitability of mapping robots for underground geo-monitoring.In this study,a methodology for objective and quantitative assessment of the applicability of SLAM methods in underground geo-monitoring is proposed.This involves the development of an underground test field and some specific metrics,which allow detailed local accuracy analysis of point measurements,line segments,and areas using artificial targets.With this proposed methodology,a series of repeated experimental measurements has been performed with an autonomous driving robot and the selected LiDAR-and visual-based SLAM methods.The resulting point cloud was compared with the reference data measured by a total station and a terrestrial laser scanner.The accuracy and precision of the selected SLAM methods as well as the verifiability and reliability of the results are evaluated and discussed by analysing quantities such as the deviations of the control points coordinates,cloudto-cloud distances between the test and reference point cloud,normal vector,centre point coordinates and area of the planar objects.The results demonstrate that the HDL Graph SLAM achieves satisfactory precision,accuracy,and repeatability with a mean cloud-to-cloud distance of 0.12 m(with a standard deviation of 0.13 m)in an 80 m closed-loop measurement area.Although RTAB-Map exhibits better plane-capturing capabilities,the measurement results reveal instability and inaccuracies.展开更多
文摘巡检机器人对室内场景进行自主导航监测时,采用视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)方法构建的三维深度地图存在实时性不高、定位精度下降的问题。对此,提出了一种基于RGB-D相机和优化RTAB-Map(real time appearance based mapping)算法的巡检机器人视觉导航方法。首先,通过重新配置RTAB-Map点云更新频率,实现算法优化,构建稠密的点云地图后;采用启发式A*算法、动态窗口法(dynamic window approach,DWA)分别制定全局与局部巡检路径,通过自适应蒙特卡罗定位(adaptive Monte Carlo localization,AMCL)方法更新机器人的实时位姿信息,再将搭建好的实体巡检机器人在软件、硬件平台上完成视觉导航测试实验。结果表明:优化后的RTAB-Map算法运行时的内存占比稍有增加,但获得与真实环境一致性更高的三维深度地图,在一定程度上提高视觉导航的准确性与实用性。
文摘在基于视觉的即时定位与建图(Simultaneous Localization and Mapping,SLAM)中,RTAB-Map是一个比较经典的解决方案,它包含有鲁棒的视觉里程计,同时也提供稠密点云地图、2D占据栅格地图和Octomap(3D占据栅格地图)三种地图构建形式。但稠密点云地图数据量大,无法适用于机器人导航;2D占据栅格地图虽数据量小,但无法反映复杂地形特征,一般只用于室内扫地机器人导航;Octomap能较好地反映三维空间内障碍物的信息,多用于无人机的导航,但对于地面移动机器人来说存在信息冗余。为RTAB-Map扩展了2.5D高程栅格地图构建模块,这种地图可以很好地反映地形环境特征,且地图所占用存储空间更小,更能充分利用移动机器人有限的存储和计算资源。
基金supported by the German Academic Scholarship Foundation,the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation,Project number 422117092)the Saxon Ministry of Science and Arts.
文摘Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely.However,this task inherently exposes monitoring staff to hazardous environments,especially in underground settings.Since 2000s,robots have been widely applied in various fields and many studies have focused on establishing autonomous mobile robotic systems as well as solving the issue of underground navigation and mapping.However,only a few studies have conducted quantitative evaluations of these methods,and almost none have provided a systematic and comprehensive assessment of the suitability of mapping robots for underground geo-monitoring.In this study,a methodology for objective and quantitative assessment of the applicability of SLAM methods in underground geo-monitoring is proposed.This involves the development of an underground test field and some specific metrics,which allow detailed local accuracy analysis of point measurements,line segments,and areas using artificial targets.With this proposed methodology,a series of repeated experimental measurements has been performed with an autonomous driving robot and the selected LiDAR-and visual-based SLAM methods.The resulting point cloud was compared with the reference data measured by a total station and a terrestrial laser scanner.The accuracy and precision of the selected SLAM methods as well as the verifiability and reliability of the results are evaluated and discussed by analysing quantities such as the deviations of the control points coordinates,cloudto-cloud distances between the test and reference point cloud,normal vector,centre point coordinates and area of the planar objects.The results demonstrate that the HDL Graph SLAM achieves satisfactory precision,accuracy,and repeatability with a mean cloud-to-cloud distance of 0.12 m(with a standard deviation of 0.13 m)in an 80 m closed-loop measurement area.Although RTAB-Map exhibits better plane-capturing capabilities,the measurement results reveal instability and inaccuracies.
