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Three-dimensional monocular pose measurement using computational neural networks

机译:使用计算神经网络的三维单眼姿势测量

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Abstract: Experimental measurement of position and attitude (pose) of a rigid target using machine vision is of particular importance to autonomous robotic manipulation. Traditionally, the monocular four-point pose problem has been used which encompasses three distinct subproblems: inverse perspective; calibration of internal camera parameters; and knowledge of the pose of the camera (external camera parameters). To this end, a new unified concept for monocular pose measurement using computational neural networks has been developed which obviates the need to estimate camera parameters and which provides rapid solution of inverse perspective with compensation for nonhomogeneous lens distortion. Input neurons are (x, y) image coordinates for target landmarks. Output neurons are (X, Y, Z, roll, pitch, yaw) target position and attitude relative to an external reference frame. Modified back-propagation has been used to train the neural network using both synthetic and experimental training sets for comparison to current four-point pose methods. Recommendations are provided for number of neural layers, number of neurons per layer, and richness versus breadth of pose training sets.!9
机译:摘要:使用机器视觉对刚性目标的位置和姿态(姿势)进行实验测量对于自主机器人操纵尤为重要。传统上,使用了单眼四点姿势问题,它包含三个不同的子问题:逆向透视;逆向透视;逆向透视。校准内部摄像机参数;并了解相机的姿势(相机的外部参数)。为此,已经开发了用于使用计算神经网络的单眼姿势测量的新的统一概念,该概念消除了估计照相机参数的需要,并且提供了反向视角的快速解决方案,并补偿了非均匀镜头畸变。输入神经元是目标地标的(x,y)图像坐标。输出神经元是相对于外部参考系的(X,Y,Z,侧倾,俯仰,偏航)目标位置和姿态。修改后的反向传播已用于使用合成和实验训练集来训练神经网络,以与当前的四点姿势方法进行比较。提供了有关神经层数量,每层神经元数量以及姿势训练集的丰富度与广度的建议。9

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