Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speed...Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.展开更多
在基于增强现实装配引导的复杂零/部件装配场景中,由于手部对零/部件的遮挡,导致零件位姿解算时产生较大的误差,甚至造成求解失败。目前针对手工装配零件的位姿估计算法在解决零件遮挡问题时没有考虑手部信息,使得位姿估计精度难以满足...在基于增强现实装配引导的复杂零/部件装配场景中,由于手部对零/部件的遮挡,导致零件位姿解算时产生较大的误差,甚至造成求解失败。目前针对手工装配零件的位姿估计算法在解决零件遮挡问题时没有考虑手部信息,使得位姿估计精度难以满足增强装配实际应用的要求。针对上述问题,提出了融合手部姿态的零件6D位姿估计算法,即HandICG算法。该算法将手部的姿态信息与迭代对应几何(Iterative Corresponding Geometry,ICG)算法进行融合,当发生手部遮挡时,将手部的姿态信息应用到零件姿态的求解中,从而显著提高手部遮挡情况下零件位姿估计的精度,实验表明,平均模型点距离(Average Distance of Model points,ADD)相关评价指标达到74.73%,是ICG算法的2.61倍。该算法显著提升了增强装配场景中零件位姿解算的准确性和鲁棒性。展开更多
基金Supported by the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.
文摘在基于增强现实装配引导的复杂零/部件装配场景中,由于手部对零/部件的遮挡,导致零件位姿解算时产生较大的误差,甚至造成求解失败。目前针对手工装配零件的位姿估计算法在解决零件遮挡问题时没有考虑手部信息,使得位姿估计精度难以满足增强装配实际应用的要求。针对上述问题,提出了融合手部姿态的零件6D位姿估计算法,即HandICG算法。该算法将手部的姿态信息与迭代对应几何(Iterative Corresponding Geometry,ICG)算法进行融合,当发生手部遮挡时,将手部的姿态信息应用到零件姿态的求解中,从而显著提高手部遮挡情况下零件位姿估计的精度,实验表明,平均模型点距离(Average Distance of Model points,ADD)相关评价指标达到74.73%,是ICG算法的2.61倍。该算法显著提升了增强装配场景中零件位姿解算的准确性和鲁棒性。
