  Multi-modal depth estimation is one of the key challenges for endowing
autonomous machines with robust robotic perception capabilities. There have
been outstanding advances in the development of uni-modal depth estimation
techniques based on either monocular cameras, because of their rich resolution,
or LiDAR sensors, due to the precise geometric data they provide. However, each
of these suffers from some inherent drawbacks, such as high sensitivity to
changes in illumination conditions in the case of cameras and limited
resolution for the LiDARs. Sensor fusion can be used to combine the merits and
compensate for the downsides of these two kinds of sensors. Nevertheless,
current fusion methods work at a high level. They process the sensor data
streams independently and combine the high-level estimates obtained for each
sensor. In this paper, we tackle the problem at a low level, fusing the raw
sensor streams, thus obtaining depth estimates which are both dense and
precise, and can be used as a unified multi-modal data source for higher level
estimation problems.
  This work proposes a Conditional Random Field model with multiple geometry
and appearance potentials. It seamlessly represents the problem of estimating
dense depth maps from camera and LiDAR data. The model can be optimized
efficiently using the Conjugate Gradient Squared algorithm. The proposed method
was evaluated and compared with the state-of-the-art using the commonly used
KITTI benchmark dataset.
