基于卷积神经网络(CNN)的目标位姿估计模型的损失函数大多采用两点之间的欧氏距离作为评判准则,虽然该损失函数计算简单、运算速度快,但训练规则不够全面、缺乏对目标的全局认识。针对这一问题,提出了一种基于组合型损失函数的ComPoseNet模型,并进行位姿估计。此模型中的损失函数从空间学习的角度出发,同时利用两点欧氏距离、两点直线和两点直线角度等作为训练规则。相比传统损失函数,此算法分别从点、线以及角度方面考虑了目标的空间整体位置,进一步减小了估计位姿与真实位姿之间的误差,位姿估计得以改善。在LineMod数据上进行大量的实验和分析,结果表明,在相同的训练次数情况下,本文算法比传统算法收敛速度快、精度高、误差小,其中平移误差降低了7.407%,角度误差降低了6.968%。

The loss function of a target pose estimation model based on a convolutional neural network (CNN) mostly uses the Euclidean distance between two points as the evaluation criterion. Although the loss function is simple in calculation and fast in operation, the training rules are not comprehensive enough and lack global understanding of the target. In this paper, a ComPoseNet model based on a combined loss function is proposed for pose estimation. The loss function in this model is based on spatial learning, and the two-point Euclidean distance, straight line, and straight line angle are used as training rules. Compared with the traditional loss function, this algorithm considers the spatial position of the target from the point, line, and angle, reducing the error between the estimated and the real poses so that the effect of the pose estimation is improved. Numerous experiments and analysis of LineMod data show that the algorithm has a higher convergence speed, greater accuracy, and smaller errors than the traditional algorithm operating for the same training times. The translation error is reduced by 7.407%, and the angle error is reduced by 6.968%.