  Depth perception is considered an invaluable source of information in the
context of 3D mapping and various robotics applications. However, point cloud
maps acquired using consumer-level light detection and ranging sensors (lidars)
still suffer from bias related to local surface properties such as measuring
beam-to-surface incidence angle, distance, texture, reflectance, or
illumination conditions. This fact has recently motivated researchers to
exploit traditional filters, as well as the deep learning paradigm, in order to
suppress the aforementioned depth sensors error while preserving geometric and
map consistency details. Despite the effort, depth correction of lidar
measurements is still an open challenge mainly due to the lack of clean 3D data
that could be used as ground truth. In this paper, we introduce two novel point
cloud map consistency losses, which facilitate self-supervised learning on real
data of lidar depth correction models. Specifically, the models exploit
multiple point cloud measurements of the same scene from different view-points
in order to learn to reduce the bias based on the constructed map consistency
signal. Complementary to the removal of the bias from the measurements, we
demonstrate that the depth correction models help to reduce localization drift.
Additionally, we release a data set that contains point cloud data captured in
an indoor corridor environment with precise localization and ground truth
mapping information.
