Planning Sensing Sequences for Subsurface 3D Tumor Mapping

@article{Cho2021PlanningSS,
  title={Planning Sensing Sequences for Subsurface 3D Tumor Mapping},
  author={Brian Cho and Tucker Hermans and Alan Kuntz},
  journal={2021 International Symposium on Medical Robotics (ISMR)},
  year={2021},
  pages={1-7}
}
Surgical automation has the potential to enable increased precision and reduce the per-patient workload of overburdened human surgeons. An effective automation system must be able to sense and map subsurface anatomy, such as tumors, efficiently and accurately. In this work, we present a method that plans a sequence of sensing actions to map the 3D geometry of subsurface tumors. We leverage a sequential Bayesian Hilbert map to create a 3D probabilistic occupancy model that represents the… 

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