  Traditional dense volumetric representations for robotic mapping make
simplifying assumptions about sensor noise characteristics due to computational
constraints. We present a framework that, unlike conventional occupancy grid
maps, explicitly models the sensor ray formation for a depth sensor via a
Markov Random Field and performs loopy belief propagation to infer the marginal
probability of occupancy at each voxel in a map. By explicitly reasoning about
occlusions our approach models the correlations between adjacent voxels in the
map. Further, by incorporating learnt sensor noise characteristics we perform
accurate inference even with noisy sensor data without ad-hoc definitions of
sensor uncertainty. We propose a new metric for evaluating probabilistic
volumetric maps and demonstrate the higher fidelity of our approach on
simulated as well as real-world datasets.
