提出了一种双目移动机器人实时动态目标识别与定位方法。该算法首先采用SIFT(Scale Invariant Features Transforms)算法提取目标特征,并结合双目视差特征进行目标匹配;然后通过区域增长方法进行目标区域的提取;最后结合双目视觉标定的...提出了一种双目移动机器人实时动态目标识别与定位方法。该算法首先采用SIFT(Scale Invariant Features Transforms)算法提取目标特征,并结合双目视差特征进行目标匹配;然后通过区域增长方法进行目标区域的提取;最后结合双目视觉标定的模型对目标进行定位。实验结果表明:该方法在摄像机运动-目标运动情况下,能对局部特征未知或特征不明显的动态目标进行有效的识别与定位。展开更多
In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multip...In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multiple small-scale underwater robots. Therefore, this paper proposes a novel RGB-D camera and Inertial Measurement Unit (IMU) fusion-based cooperative and relative close-range localization approach for special environments, such as underwater caves. Owing to the rotation movement with zero-radius, the cooperative localization of Multiple Turtle-inspired Amphibious Spherical Robot (MTASRs) is realized. Firstly, we present an efficient Histogram of Oriented Gradient (HOG) and Color Names (CNs) fusion feature extracted from color images ofTASRs. Then, by training Support Vector Machine (SVM) classifier with this fusion feature, an automatic recognition method of TASRs is developed. Secondly, RGB-D camerabased measurement model is obtained by the depth map In order to realize the cooperative and relative close-range localization of MTASRs, the MTASRs model is established with RGB-D camera and IMU. Finally, the depth measurement in water is corrected and the efficiency of RGB-D camera for underwater application is validated. Then experiments of our proposed localization method with three robots were conducted and the results verified the feasibility of the proposed method for MTASRs.展开更多
为了能更加有效快速地进行眼睛定位,提出了一种基于块(based on blocks)的眼睛定位新算法,该算法首先将图像二值化并划分为块,然后利用两只眼睛的相似性和眼睛对的唯一性将一系列的图像块进行匹配,并以此确定一对眼睛的位置。由于该新...为了能更加有效快速地进行眼睛定位,提出了一种基于块(based on blocks)的眼睛定位新算法,该算法首先将图像二值化并划分为块,然后利用两只眼睛的相似性和眼睛对的唯一性将一系列的图像块进行匹配,并以此确定一对眼睛的位置。由于该新算法利用了两眼睛固有的位置关系与相互间的相似性,将两眼睛成对考虑,所以能检测各种角度(平面旋转)人脸的眼睛,并可得到较高的检测准确率。此外,实验显示该算法在一定程度上还能适应不同光照条件和表情变化。大量人脸图片上的实验结果表明,该算法可以可靠?快速地定位眼睛。展开更多
文摘提出了一种双目移动机器人实时动态目标识别与定位方法。该算法首先采用SIFT(Scale Invariant Features Transforms)算法提取目标特征,并结合双目视差特征进行目标匹配;然后通过区域增长方法进行目标区域的提取;最后结合双目视觉标定的模型对目标进行定位。实验结果表明:该方法在摄像机运动-目标运动情况下,能对局部特征未知或特征不明显的动态目标进行有效的识别与定位。
基金the National Natural Science Foundation of China (Nos. 61773064, 61503028)Graduate Technological Innovation Project of Beijing Institute of Technology (No. 2018CX10022)National High Tech. Research and Development Program of China (No. 2015AA043202).
文摘In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multiple small-scale underwater robots. Therefore, this paper proposes a novel RGB-D camera and Inertial Measurement Unit (IMU) fusion-based cooperative and relative close-range localization approach for special environments, such as underwater caves. Owing to the rotation movement with zero-radius, the cooperative localization of Multiple Turtle-inspired Amphibious Spherical Robot (MTASRs) is realized. Firstly, we present an efficient Histogram of Oriented Gradient (HOG) and Color Names (CNs) fusion feature extracted from color images ofTASRs. Then, by training Support Vector Machine (SVM) classifier with this fusion feature, an automatic recognition method of TASRs is developed. Secondly, RGB-D camerabased measurement model is obtained by the depth map In order to realize the cooperative and relative close-range localization of MTASRs, the MTASRs model is established with RGB-D camera and IMU. Finally, the depth measurement in water is corrected and the efficiency of RGB-D camera for underwater application is validated. Then experiments of our proposed localization method with three robots were conducted and the results verified the feasibility of the proposed method for MTASRs.
文摘为了能更加有效快速地进行眼睛定位,提出了一种基于块(based on blocks)的眼睛定位新算法,该算法首先将图像二值化并划分为块,然后利用两只眼睛的相似性和眼睛对的唯一性将一系列的图像块进行匹配,并以此确定一对眼睛的位置。由于该新算法利用了两眼睛固有的位置关系与相互间的相似性,将两眼睛成对考虑,所以能检测各种角度(平面旋转)人脸的眼睛,并可得到较高的检测准确率。此外,实验显示该算法在一定程度上还能适应不同光照条件和表情变化。大量人脸图片上的实验结果表明,该算法可以可靠?快速地定位眼睛。
