  Pose estimation is a fundamental building block for robotic applications such
as autonomous vehicles, UAV, and large scale augmented reality. It is also a
prohibitive factor for those applications to be in mass production, since the
state-of-the-art, centimeter-level pose estimation often requires long mapping
procedures and expensive localization sensors, e.g. LiDAR and high precision
GPS/IMU, etc. To overcome the cost barrier, we propose a neural network based
solution to localize a consumer degree RGB camera within a prior sparse LiDAR
map with comparable centimeter-level precision. We achieved it by introducing a
novel network module, which we call resistor module, to enforce the network
generalize better, predicts more accurately, and converge faster. Such results
are benchmarked by several datasets we collected in the large scale indoor
parking garage scenes. We plan to open both the data and the code for the
community to join the effort to advance this field.
