论文标题
LIEPOSENET：基于Lie组的异质损失函数，以大幅加速Posenet训练过程
LiePoseNet: Heterogeneous Loss Function Based on Lie Group for Significant Speed-up of PoseNet Training Process
论文作者
论文摘要
视觉定位是机器人技术和计算机视觉的必不可少的现代技术。解决此任务的流行方法是基于图像的方法。如今，这些方法的精度较低，训练时间很长。原因是缺乏刚体和投射几何意识，地标对称性和均匀的错误假设。我们提出了基于与谎言组的浓缩高斯分布来克服这些困难的异质损失函数。经过实验，提出的方法使我们能够以可接受的误差值显着加快训练过程（从300至10个时期）。
Visual localization is an essential modern technology for robotics and computer vision. Popular approaches for solving this task are image-based methods. Nowadays, these methods have low accuracy and a long training time. The reasons are the lack of rigid-body and projective geometry awareness, landmark symmetry, and homogeneous error assumption. We propose a heterogeneous loss function based on concentrated Gaussian distribution with the Lie group to overcome these difficulties. Following our experiment, the proposed method allows us to speed up the training process significantly (from 300 to 10 epochs) with acceptable error values.
