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Multi-modal depth estimation is one of the key challenges for endowingautonomous machines with robust robotic perception capabilities. There havebeen outstanding advances in the development of uni-modal depth estimationtechniques based on either monocular cameras, because of their rich resolution,or LiDAR sensors, due to the precise geometric data they provide. However, eachof these suffers from some inherent drawbacks, such as high sensitivity tochanges in illumination conditions in the case of cameras and limitedresolution for the LiDARs. Sensor fusion can be used to combine the merits andcompensate for the downsides of these two kinds of sensors. Nevertheless,current fusion methods work at a high level. They process the sensor datastreams independently and combine the high-level estimates obtained for eachsensor. In this paper, we tackle the problem at a low level, fusing the rawsensor streams, thus obtaining depth estimates which are both dense andprecise, and can be used as a unified multi-modal data source for higher levelestimation problems.  This work proposes a Conditional Random Field model with multiple geometryand appearance potentials. It seamlessly represents the problem of estimatingdense depth maps from camera and LiDAR data. The model can be optimizedefficiently using the Conjugate Gradient Squared algorithm. The proposed methodwas evaluated and compared with the state-of-the-art using the commonly usedKITTI benchmark dataset.

Probabilistic Multimodal Depth Estimation Based on Camera-LiDAR Sensor Fusion