  Robotic surgical subtask automation has the potential to reduce the
per-patient workload of human surgeons. There are a variety of surgical
subtasks that require geometric information of subsurface anatomy, such as the
location of tumors, which necessitates accurate and efficient surgical sensing.
In this work, we propose an automated sensing method that maps 3D subsurface
anatomy to provide such geometric knowledge. We model the anatomy via a
Bayesian Hilbert map-based probabilistic 3D occupancy map. Using the 3D
occupancy map, we plan sensing paths on the surface of the anatomy via a graph
search algorithm, $A^*$ search, with a cost function that enables the
trajectories generated to balance between exploration of unsensed regions and
refining the existing probabilistic understanding. We demonstrate the
performance of our proposed method by comparing it against 3 different methods
in several anatomical environments including a real-life CT scan dataset. The
experimental results show that our method efficiently detects relevant
subsurface anatomy with shorter trajectories than the comparison methods, and
the resulting occupancy map achieves high accuracy.
