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长春理工大学 电子信息工程学院, 吉林 长春 130022
Received:09 November 2018,
Accepted:05 April 2019,
Published:05 June 2019
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Yang YU, Yan PIAO, Yan NI, et al. Precise location of indoor objects based on ROS and point cloud images[J]. Chinese journal of liquid crystals and displays, 2019, 34(6): 598-604.
Yang YU, Yan PIAO, Yan NI, et al. Precise location of indoor objects based on ROS and point cloud images[J]. Chinese journal of liquid crystals and displays, 2019, 34(6): 598-604. DOI: 10.3788/YJYXS20193406.0598.
考虑到ROS SLAM构建的地图只能描述环境的二维信息,三维点云图像只能描述物体独立的三维信息等特点,本文融合了ROS SLAM的Gmapping算法构建的室内二维地图与物体的三维点云图像信息,提出了一种复合坐标定位系统。首先对不同室内进行分类,进行一维坐标的标定,其次通过对Gmapping算法构建好的地图等进行二三维坐标标定,再结合空间信息构成外部坐标系
φ
,最后通过对采集到的物体三维点云坐标进行仿射运算获得物体基于外部坐标三维坐标,结合一维坐标,对物体进行复合四维坐标定位。整个定位实验数据表明,物体室内的位置平均测量误差只有4.2 cm,其定位精度比起常见的超声波与红外线定位系统提高6.7%,比基于蓝牙角度测量的定位系统定位精度提高20%,比超宽带定位系统提高72%。物体定位误差小,定位精准。
Considering that the map constructed by ROS SLAM can only describe the two-dimensional information of the environment
the three-dimensional point cloud image can only describe the independent three-dimensional information of the object
this paper combines the information of indoor two-dimensional map and object's three-dimensional point cloud image constructed by ROS SLAM's Gmapping algorithm
and proposes a composite coordinate positioning system. Firstly
different indoors are classified and one-dimensional coordinates are calibrated. Secondly
two-dimensional and three-dimensional coordinates are calibrated by the map constructed by Gmapping algorithm
and then external coordinates are formed by combining spatial information. Finally
three-dimensional coordinates of the collected object are obtained by affine operation based on external coordinates. Combined with one-dimensional coordinates
the object is combined with four dimensional coordinates. The whole positioning experiment data show that the average error of indoor position measurement is only 4.2 cm
and the positioning accuracy is 6.7% higher than that of the common ultrasonic and infrared positioning systems
20% higher than that of the positioning system based on Bluetooth angle measurement
and 72% higher than that of the ultra-wideband positioning system. The object location error is small and the location is precise.
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