  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.
