3D SLAM的室内背包移动测量系统研究
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摘要
针对室内移动测图中GPS信号缺失,导致无法获取精确定位坐标与导航的问题,该文研发了一套室内背包式移动测量系统,该系统能够利用三维激光定位与测图的方法实现快速同步定位及地图创建和室内三维激光点云获取,可以应用在室内或地下的环境中进行数据采集,在不损失作业效率的前提下获取系统作业轨迹、高精度点云等多种数据结果。为了对系统的精度进行验证,使用该系统在实验室大楼走廊中进行实验获取室内三维激光点云,并将实验结果与采用常规测量手段得到的结果进行对比分析。实验表明,此系统的相对精度和绝对精度分别能够达到0.048和0.047m,在室内三维信息获取过程中相比较于传统作业方式显著提高了作业效率和数据质量,能够满足室内建模与测图要求。
In order to solve the problem of missing GPS signal in indoor mobile mapping,this leads to failure to obtain the precise position and navigation information.This paper develops a set of indoor backpacked mobile measurement system.This system of using 3Dlaser simultaneous localization and mapping to finish localization and mapping,and acquire indoor point cloud.It also can be used in data collection of the indoor or underground environment,and obtaining the working trajectory,high-precision point cloud and other data without losing operating efficiency.In order to test this system,the experiment is carried out in the science building,and indoor 3D point cloud is acquired in the laboratory and corridor.The experimental data are compared with the data obtained by conventional measurement methods.The results show that the method and equipment can significantly improve the operation efficiency and data quality compared with the traditional operation mode in the process of obtaining 3D indoor information,The verification can be confirmed the relative accuracy of the point data is 0.048 m and the absolute precision is 0.047 m,which meets the requirements of indoor modeling and mapping.
In order to solve the problem of missing GPS signal in indoor mobile mapping,this leads to failure to obtain the precise position and navigation information.This paper develops a set of indoor backpacked mobile measurement system.This system of using 3Dlaser simultaneous localization and mapping to finish localization and mapping,and acquire indoor point cloud.It also can be used in data collection of the indoor or underground environment,and obtaining the working trajectory,high-precision point cloud and other data without losing operating efficiency.In order to test this system,the experiment is carried out in the science building,and indoor 3D point cloud is acquired in the laboratory and corridor.The experimental data are compared with the data obtained by conventional measurement methods.The results show that the method and equipment can significantly improve the operation efficiency and data quality compared with the traditional operation mode in the process of obtaining 3D indoor information,The verification can be confirmed the relative accuracy of the point data is 0.048 m and the absolute precision is 0.047 m,which meets the requirements of indoor modeling and mapping.
引文
[1]李秀智,贾松敏.基于多源融合式SLAM的机器人三维环境建模[J].北京理工大学学报,2015,35(3):262-267.(LI Xiuzhi,JIA Songmin.3D map building for mobile robot based on multi-sensor fusion aided SLAM[J].Transactions of Beijing Institute of Technology,2015,35(3):262-267.)
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[3]李昀泽.基于激光雷达的室内机器人SLAM研究[D].广州:华南理工大学,2016.(LI Yunze.Research on SLAM of indoor robot based on LiDAR[D].Guangzhou:South China University of Technology,2016.)
[4]张毅,杜凡宇,罗元,等.一种融合激光和深度视觉传感器的SLAM地图创建方法[J].计算机应用研究,2016,33(10):2970-2972.(ZHANG Yi,DU Fanyu,LUO Yuan,et al.Map-building approach based on laser and depth visual sensor fusion SLAM[J].Application Research of Computers,2016,33(10):2970-2972.)
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[6]朱福利.基于SLAM的移动机器人室内环境感知和地图构建研究[D].广州:广东工业大学,2016.(ZHU Fuli.Research on indoor environment perception and map building of mobile robot based on SLAM[D].Guangzhou:Guangdong University of Technology,2016.)
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[9]闫金金.面向室内行人定位的制图与定位精度评估方法[D].武汉:中国地质大学,2016.(YAN Jinjin.Indoor mapping and accuracy evaluation for pedestrian location[D].Wuhan:China University of Geosciences,2016.)
[10]WANG D,LIANG H,MEI T,et al.LiDAR Scan matching EKF-SLAM using the differential model of vehicle motion[J].Institute of Advanced Manufacturing Technology,Hefei Institute of Physical Science.CAS,2013,36(1):908-912.
[11]CARRILLO H,REID I,CASTELLANOS J A.On the comparison of uncertainty criteria for active SLAM[C]//IEEE International Conference on Robotics and Automation.[S.l.]:IEEE,2012:2080-2087.
[12]CHOUDHARY S,CARLONE L,NIETO C,et al.Distributed trajectory estimation with privacy and communication constraints:a two-stage distributed Gauss-Seidel approach[C]//IEEE International Conference on Robotics and Automation.[S.l.]:IEEE,2016.
[13]CHURCHILL W,NEWMAN P.Experience-based navigation for long-term localisation[J].International Journal of Robotics Research,2013,32(14):1645-1661.
[14]POLLEFEYS M.Discovering and exploiting 3D symmetries in structure from motion[C]//IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Computer Society,2012:1514-1521.
[15]DAME A,PRISACARIU V A,REN C Y,et al.Dense Reconstruction using 3D object shape priors[C]//IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Computer Society,2013:1288-1295.
[16]DONG J,NELSON E,INDELMAN V,et al.Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach[C]//IEEE International Conference on Robotics and Automation.[S.l.]:IEEE,2015:5807-5814.
[2]KOHLBRECHER S,STRYK O V,MEYER J,et al.A flexible and scalable SLAM system with full 3Dmotion estimation[C]//IEEE International Symposium on Safety,Security,and Rescue Robotics.[S.l.]:IEEE,2011:155-160.
[3]李昀泽.基于激光雷达的室内机器人SLAM研究[D].广州:华南理工大学,2016.(LI Yunze.Research on SLAM of indoor robot based on LiDAR[D].Guangzhou:South China University of Technology,2016.)
[4]张毅,杜凡宇,罗元,等.一种融合激光和深度视觉传感器的SLAM地图创建方法[J].计算机应用研究,2016,33(10):2970-2972.(ZHANG Yi,DU Fanyu,LUO Yuan,et al.Map-building approach based on laser and depth visual sensor fusion SLAM[J].Application Research of Computers,2016,33(10):2970-2972.)
[5]KONOLIGE K,GRISETTI G,KUMMERLE R,et al.Efficient sparse pose adjustment for 2Dmapping[C]//Ieee/rsj International Conference on Intelligent Robots and Systems.[S.l.]:IEEE,2010:22-29.
[6]朱福利.基于SLAM的移动机器人室内环境感知和地图构建研究[D].广州:广东工业大学,2016.(ZHU Fuli.Research on indoor environment perception and map building of mobile robot based on SLAM[D].Guangzhou:Guangdong University of Technology,2016.)
[7]GUERRERO C J F,MADRIGAL S H,DURAND S,et al.Design and implementation of an attitude and heading reference system(AHRS)[C]//International Conference on Electrical Engineering Computing Science and Automatic Control.[S.l.]:IEEE,2011:1-5.
[8]STEUX B,HAMZAOUI O E.tinySLAM:A SLAM algorithm in less than 200lines C-language program[C]//International Conference on Control Automation Robotics &Vision.[S.l.]:IEEE,2011:1975-1979.
[9]闫金金.面向室内行人定位的制图与定位精度评估方法[D].武汉:中国地质大学,2016.(YAN Jinjin.Indoor mapping and accuracy evaluation for pedestrian location[D].Wuhan:China University of Geosciences,2016.)
[10]WANG D,LIANG H,MEI T,et al.LiDAR Scan matching EKF-SLAM using the differential model of vehicle motion[J].Institute of Advanced Manufacturing Technology,Hefei Institute of Physical Science.CAS,2013,36(1):908-912.
[11]CARRILLO H,REID I,CASTELLANOS J A.On the comparison of uncertainty criteria for active SLAM[C]//IEEE International Conference on Robotics and Automation.[S.l.]:IEEE,2012:2080-2087.
[12]CHOUDHARY S,CARLONE L,NIETO C,et al.Distributed trajectory estimation with privacy and communication constraints:a two-stage distributed Gauss-Seidel approach[C]//IEEE International Conference on Robotics and Automation.[S.l.]:IEEE,2016.
[13]CHURCHILL W,NEWMAN P.Experience-based navigation for long-term localisation[J].International Journal of Robotics Research,2013,32(14):1645-1661.
[14]POLLEFEYS M.Discovering and exploiting 3D symmetries in structure from motion[C]//IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Computer Society,2012:1514-1521.
[15]DAME A,PRISACARIU V A,REN C Y,et al.Dense Reconstruction using 3D object shape priors[C]//IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Computer Society,2013:1288-1295.
[16]DONG J,NELSON E,INDELMAN V,et al.Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach[C]//IEEE International Conference on Robotics and Automation.[S.l.]:IEEE,2015:5807-5814.