  In this work, we present a method for a probabilistic fusion of external
depth and onboard proximity data to form a volumetric 3-D map of a robot's
environment. We extend the Octomap framework to update a representation of the
area around the robot, dependent on each sensor's optimal range of operation.
Areas otherwise occluded from an external view are sensed with onboard sensors
to construct a more comprehensive map of a robot's nearby space. Our simulated
results show that a more accurate map with less occlusions can be generated by
fusing external depth and onboard proximity data.
