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工厂环境下改进的视觉SLAM算法

Improved Visual SLAM Algorithm in Factory Environment
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摘要 针对工业场景对自动导引车(AGV)高定位精度的要求,提出一种改进的视觉同时定位与地图创建(VSLAM)算法.在算法前端,双目相机采集立体图像,通过双目匹配算法得到亚像素级的匹配点对,计算出这些特征点在相机坐标系中的3D信息.然后利用RansacPnP算法根据3D-2D匹配点对计算位姿变换,并以它为初值进一步最小化重投影误差,实现局部优化.基于匹配点对描述子的汉明距离提出一种不确定性模型,该模型为局部优化中的约束条件提供信息矩阵,提高定位精度.在算法的后端,通过竖直向上拍摄的单目相机检测可靠的人工信标闭环信息,进行全局位姿优化,并针对AGV的运动模型和工作场景,提出一种基于全局平面约束的优化方法,降低SLAM系统的误差.实验通过KITTI离线数据集对比了该算法前端双目里程计与ORB-SLAM2及libviso2算法里程计的定位精度,并在工厂环境中对整个算法进行实地测试来判断其实际精度和鲁棒性.实验结果表明该算法具有良好的综合性能,定位误差在10cm以内,定位频率达20Hz,能够满足工业现场要求. Aiming at the high positioning accuracy of automatic guided vehicle (AGV)in industrial scene,an improved visual simultaneous localization and mapping (VSLAM)algorithm is proposed.In the front end of the algorithm,the stereo images are captured using a binocular camera and sub-pixel-level matching point pairs are obtained through binocular matching algorithm.And the 3D information of these feature points is calculated in the camera coordinate system.Then the pose transformation is acquired using RansacPnP algorithm based on the 3D-2D matching point pairs.And the transformation is used as the initial value to do the local optimization by minimizing re-projection errors.Additionally,an uncertainty model is proposed based on the Hamming distance between the descriptors of matching point pairs.The model provides the information matrix for the constraints in the local optimization,which can improve the positioning accuracy.In the back end of the algorithm,reliable closed-loop information of artificial landmarks is detected using the monocular camera shooting vertically upwards,and then the global pose optimization is conducted.According to the motion model and working scene of the AGV,an optimization method based on global plane constraint is proposed to reduce the error of SLAM system.In the experiment,the accuracy of binocular odometer in the front end of the proposed algorithm is compared with the odometers in ORB-SLAM2 and libviso2 algorithm using the offline KITTI dataset.The online experiments in the factory environment are conducted to test the real localization accuracy and robustness of the whole system.The results show that the algorithm is of good overall performance,and its positioning errors are within 10 cm,while the positioning frequency can reach 20 Hz, which can meet the industrial requirements.
作者 李月华 朱世强 于亦奇 LI Yuehua;ZHU Shiqiang;YU Yiqi(School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China;Ocean College,Zhejiang University,Zhoushan 316021,China)
出处 《机器人》 EI CSCD 北大核心 2019年第1期95-103,共9页 Robot
基金 国家自然科学基金创新群体基金(51521064).
关键词 同时定位与建图(SLAM) 视觉里程计 自动导引车(AGV) 视觉SLAM 平面约束 simultaneous localization and mapping (SLAM) visual odometry automated guided vehicle (AGV) VSLAM (visual SLAM) plane constraint
作者简介 通信作者:李月华,11325066@zju.edu.cn.
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