基于单目天花板视觉的扫地机器人定位算法设计及实现
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摘要
随着扫地机器人的逐渐普及,通过视觉传感器进行定位已成为亟待解决的关键问题.但由于嵌入式系统资源有限导致视觉定位实现技术难度大且效果差,因此采用单目相机顶视室内环境天花板的方法,能在减小计算量的同时解决单目尺度不定性的问题.首先,对采集的连续帧采用FAST结合Shi-Tomasi算法提取特征点,并使用ORB匹配算法进行匹配,结合视觉定位模型估计出全局定位位姿.然后,辅助使用价格低廉的传感器提供粗略数据,改进了ORB匹配算法.最后,设计了特征地图进行可视化实验验证,结果表明所提方案具有较好的定位精度与实时性.
Vision Localization has become a crucial problem to be solved with the continuous popularity of sweeping robots.However,due to the limited resources of embedded systems,it is difficult to be realized and we can hardly get good performance.To address this challenge,in this paper the monocular camera which faces the ceiling is used to reduce the computational complexity and solve the uncertainty of monocular scale.Firstly,we extract and match the feature points of collected consecutive frames by using the FAST with Shi-Tomasi algorithm and ORB matching algorithm,and jointly consider the vision localization model to estimate the global positions.Then,the ORB matching algorithm is improved using the rough data provided by inexpensive sensors.Finally,we design a UI of feature map to perform visual experiments and the results show that our proposed scheme has high accuracy and low computational complexity.
Vision Localization has become a crucial problem to be solved with the continuous popularity of sweeping robots.However,due to the limited resources of embedded systems,it is difficult to be realized and we can hardly get good performance.To address this challenge,in this paper the monocular camera which faces the ceiling is used to reduce the computational complexity and solve the uncertainty of monocular scale.Firstly,we extract and match the feature points of collected consecutive frames by using the FAST with Shi-Tomasi algorithm and ORB matching algorithm,and jointly consider the vision localization model to estimate the global positions.Then,the ORB matching algorithm is improved using the rough data provided by inexpensive sensors.Finally,we design a UI of feature map to perform visual experiments and the results show that our proposed scheme has high accuracy and low computational complexity.
引文
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[3]Sun Y D,Ding N,Qian H H,etal.Real-time monocular visual self-localization approach using natural circular landmarks for indoor navigation[C]∥Proc of the IEEE International Conference on Robotics and Biomimetics.IEEE,2012:495-500.
[4]Wang H,Mou W,Seet G,et al.Real-time visual odometry estimation based on principal direction detection on ceiling vision[J].International Journal of Automation and Computing,2013,10(5):397-404.
[5]Wu C J,Tsai W H.Location estimation for indoor autonomous vehicle navigation by omni-directional vision using circular landmarks on ceilings[J].Robotics and Autonomous Systems,2009,57(5):546-555.
[6]Laganiere R.OpenCV 2computer vision application programming cookbook[M].Birmingham,UK:Packt Publishing,2011.
[7]ZHAO Chenchen,GONG Zhihui,LONG Yican,et al.The oblique stereo image matching with Shi-Tomasi features[J].Journal of Geomatics Science and Technology,2015,32(1):66-71.
[8]Chen X H,Jia Y M.Indoor locallization for mobile robots using lampshade corners as landmarks:visual system calibration,feature extraction and experiments[J].International Journal of Control,Automation,and Systems,2014,12(6):1313-1322.
[9]Rublee E,Rabaud V,Konolige K,et al.ORB:An efficient alternative to SIFT or SURF[C]∥Computer Vision(ICCV),2011IEEE International Conference on.IEEE,2011:2564-2571.