  Camera localization is a fundamental and key component of autonomous driving
vehicles and mobile robots to localize themselves globally for further
environment perception, path planning and motion control. Recently end-to-end
approaches based on convolutional neural network have been much studied to
achieve or even exceed 3D-geometry based traditional methods. In this work, we
propose a compact network for absolute camera pose regression. Inspired from
those traditional methods, a 3D scene geometry-aware constraint is also
introduced by exploiting all available information including motion, depth and
image contents. We add this constraint as a regularization term to our proposed
network by defining a pixel-level photometric loss and an image-level
structural similarity loss. To benchmark our method, different challenging
scenes including indoor and outdoor environment are tested with our proposed
approach and state-of-the-arts. And the experimental results demonstrate
significant performance improvement of our method on both prediction accuracy
and convergence efficiency.
