  Localization is a critically essential and crucial enabler of autonomous
robots. While deep learning has made significant strides in many computer
vision tasks, it is still yet to make a sizeable impact on improving
capabilities of metric visual localization. One of the major hindrances has
been the inability of existing Convolutional Neural Network (CNN)-based pose
regression methods to generalize to previously unseen places. Our recently
introduced CMRNet effectively addresses this limitation by enabling map
independent monocular localization in LiDAR-maps. In this paper, we now take it
a step further by introducing CMRNet++, which is a significantly more robust
model that not only generalizes to new places effectively, but is also
independent of the camera parameters. We enable this capability by combining
deep learning with geometric techniques, and by moving the metric reasoning
outside the learning process. In this way, the weights of the network are not
tied to a specific camera. Extensive evaluations of CMRNet++ on three
challenging autonomous driving datasets, i.e., KITTI, Argoverse, and Lyft5,
show that CMRNet++ outperforms CMRNet as well as other baselines by a large
margin. More importantly, for the first-time, we demonstrate the ability of a
deep learning approach to accurately localize without any retraining or
fine-tuning in a completely new environment and independent of the camera
parameters.
