基于全局稀疏地图的AGV视觉定位技术
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摘要
为了实现自动导引车(AGV)在复杂工业环境下的高精度定位,克服环境变化给定位带来的影响,提出了基于全局稀疏地图的视觉定位方法。首先,设计了大容量二维编码点,作为人工路标铺设在工业环境的地面;然后,基于一种四边形识别算法,在复杂工业环境中准确分割和识别二维编码点;最后,利用二维编码点提供的编码信息,鲁棒匹配图像中的特征点,并以此为基础,使用一种分参数块优化的三维重建策略,实现了工业环境的大规模地图构建,为AGV视觉定位提供了一种稀疏电子地图。AGV视觉的定位通过匹配车载视觉传感器图像中的特征点和稀疏电子地图实现。停车重复定位精度小于0. 5 mm,角度偏差小于0. 5°,轨迹平均位移误差小于0. 1%。实际应用结果表明,该方法能在复杂工业环境中实现AGV视觉的定位,定位的速度和精度方面都满足工业应用的要求,为AGV的视觉定位提供了新的思路。
In order to realize the high-precision localization of automated guided vehicle( AGV) in complex industrial environment and overcome the influence of environment change,a vision localization method based on a global sparse map was proposed. First,a large-capacity two-dimensional coded point was designed,which was set on the ground as an artificial landmark. Based on a quad recognition algorithm,the coded points were accurately segmented and identified in complex industrial environment. The feature points from different images were properly matched by using the coded information provided by coded points. Then,a block-optimization three-dimensional reconstruction algorithm was designed to build a map for a large-scale industrial environment,which provided a sparse electronic map for AGV visual localization. The visual localization of AGV was realized by matching the feature points from the visual sensor and sparse electronic maps.The repeated precision of AGV is less than 0. 5 mm,the angle deviation is less than 0. 5°,and the average displacement error of trajectory is less than 0. 1%. The practical application shows that the method can realize the visual localization of AGV in complex industrial environment. The speed and precision of localization both meet the requirements of industrial application,which provides a new way for vision-based localization of AGV.
In order to realize the high-precision localization of automated guided vehicle( AGV) in complex industrial environment and overcome the influence of environment change,a vision localization method based on a global sparse map was proposed. First,a large-capacity two-dimensional coded point was designed,which was set on the ground as an artificial landmark. Based on a quad recognition algorithm,the coded points were accurately segmented and identified in complex industrial environment. The feature points from different images were properly matched by using the coded information provided by coded points. Then,a block-optimization three-dimensional reconstruction algorithm was designed to build a map for a large-scale industrial environment,which provided a sparse electronic map for AGV visual localization. The visual localization of AGV was realized by matching the feature points from the visual sensor and sparse electronic maps.The repeated precision of AGV is less than 0. 5 mm,the angle deviation is less than 0. 5°,and the average displacement error of trajectory is less than 0. 1%. The practical application shows that the method can realize the visual localization of AGV in complex industrial environment. The speed and precision of localization both meet the requirements of industrial application,which provides a new way for vision-based localization of AGV.
引文
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[3]CESAR C,LUCA C,HENRY C,et al.Past,present,and future of simultaneous localization and mapping:Toward the robustperception age[J].IEEE Transactions on Robotics,2016,32(6):1309-1332.
[4]RUBLEE E,RABAUD V,KONOLIG K,et al.ORB:An efficient alternative to SIFT or SURF[C]∥International Conference on Computer Vision.Piscataway,NJ:IEEE Press,2012:2564-2571.
[5]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[6]LU Y,SONG D.Visual navigation using heterogeneous landmarks and unsupervised geometric constraints[J].IEEE Transactions on Robotics,2017,31(3):736-749.
[7]ROLAND S,ILLAH R N,DAVIDE S,et al.Introduction to sutonomous mobile robots[M].Cambridge:MIT Press,2010:345-346.
[8]曹天扬,蔡元浩,东方明,等.结合图像内容匹配的机器人视觉导航定位与全局地图构建系统[J].光学精密工程,2017,25(8):2222-2232.CAO T Y,CAI Y H,DONG F M,et al.Robot vision system for keyframe global map establishment and robot localization based on graphic content mathing[J].Optics and Precision Engineering,2017,25(8):2222-2232(in Chinese).
[9]WULF O,LECKING D,WAGNER B.Robust self-localization in industrial environments based on 3D ceiling structures[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems.Piscataway,NJ:IEEE Press,2007:1530-1534.
[10]DAVIDE R,ROBERTO O,CRISTIAN S,et al.AGV global localization using indistinguishable artificial landmarks[C]∥IEEE International Conference on Robotics and Automation.Piscataway,NJ:IEEE Press,2011:287-292.
[11]杨剑,韩建栋,秦品乐.视觉测量中可纠错的编码点识别及提取[J].光学精密工程,2012,20(10):2293-2299.YANG J,HAN J D,QIN P L.Correcting error on recognition of coded points for photogrammetry[J].Optics and Precision Engineering,2012,20(10):2293-2299(in Chinese).
[12]段康容,刘先勇.摄影测量中编码标记点检测算法研究[J].传感器与微系统,2010,29(8):74-77.DUAN K R,LIU X Y.Detection algorithm on circular encoded point in photogrammetry[J].Transducer and Microsystem Technologies,2010,29(8):74-77(in Chinese).
[13]CANNY J.A computational approach to edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,PAMI-8(6):679-698.
[14]HARRIS C,STEPHENS M.A combined corner and edge detector[C]∥Proceedings of the 4th Alvey Vision Conference,1988:147-151.
[15]ANDERSON K R.A reevaluation of an efficient algorithm for determining the convex hull of a finite planar set[J].Information Processing Letters,1978,7(1):53-55.
[16]RICHARD H,ANDREW Z.Multiple view geometry in computer vision[M].Cambridge:Cambridge University Press,2004:159-173.
[17]ANDREAS G,PHILIP L,RAQUEL U.Are we ready for autonomous driving?The KITTI vision benchmark suite[C]∥2012IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE Press,2012:3354-3361.