Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration whe...Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.展开更多
目前智能清洁机器人的清洁覆盖率的测试主要采用单目视觉方法,针对其测量范围小、获取信息不完整、测量精度低等缺点,设计并实现了基于双目视觉特征跟踪算法的清洁机器人清洁性能测试系统。该系统采用摄像机、智能清洁机器人为硬件平台,...目前智能清洁机器人的清洁覆盖率的测试主要采用单目视觉方法,针对其测量范围小、获取信息不完整、测量精度低等缺点,设计并实现了基于双目视觉特征跟踪算法的清洁机器人清洁性能测试系统。该系统采用摄像机、智能清洁机器人为硬件平台,Ground Truth System为软件平台,运用SURF算法提取具有高鲁棒性的特征点,在后续帧中运用KLT匹配算法对特征点进行稳定跟踪,结合机器人运动路径和机器人参数实现了智能清洁机器人清洁覆盖率的测量。实验证明,该方法对智能清洁机器人的清洁覆盖率测量是快速有效的。展开更多
基金supported by the National Natural Science Foundation of China (60802043)the National Basic Research Program of China(973 Program) (2010CB327900)
文摘Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.
文摘目前智能清洁机器人的清洁覆盖率的测试主要采用单目视觉方法,针对其测量范围小、获取信息不完整、测量精度低等缺点,设计并实现了基于双目视觉特征跟踪算法的清洁机器人清洁性能测试系统。该系统采用摄像机、智能清洁机器人为硬件平台,Ground Truth System为软件平台,运用SURF算法提取具有高鲁棒性的特征点,在后续帧中运用KLT匹配算法对特征点进行稳定跟踪,结合机器人运动路径和机器人参数实现了智能清洁机器人清洁覆盖率的测量。实验证明,该方法对智能清洁机器人的清洁覆盖率测量是快速有效的。
