  Robot localization using a previously built map is essential for a variety of
tasks including highly accurate navigation and mobile manipulation. A popular
approach to robot localization is based on image-to-point cloud registration,
which combines illumination-invariant LiDAR-based mapping with economical
image-based localization. However, the recent works for image-to-point cloud
registration either divide the registration into separate modules or project
the point cloud to the depth image to register the RGB and depth images. In
this paper, we present I2PNet, a novel end-to-end 2D-3D registration network.
I2PNet directly registers the raw 3D point cloud with the 2D RGB image using
differential modules with a unique target. The 2D-3D cost volume module for
differential 2D-3D association is proposed to bridge feature extraction and
pose regression. 2D-3D cost volume module implicitly constructs the soft
point-to-pixel correspondence on the intrinsic-independent normalized plane of
the pinhole camera model. Moreover, we introduce an outlier mask prediction
module to filter the outliers in the 2D-3D association before pose regression.
Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to
increase localization accuracy. We conduct extensive localization experiments
on the KITTI Odometry and nuScenes datasets. The results demonstrate that
I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet
has a higher efficiency than the previous works and can perform the
localization in real-time. Moreover, we extend the application of I2PNet to the
camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent
approaches on the online calibration task.
