Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery

TL;DR

This paper introduces an automated, efficient method for mapping subsurface anatomy in robotic surgery using Bayesian Hilbert maps and optimized trajectory planning, improving accuracy and reducing sensing time.

Contribution

It presents a novel online sensing approach combining probabilistic 3D mapping with trajectory optimization for improved subsurface anatomy detection in robotic surgery.

Findings

Shorter sensing trajectories compared to other methods

High accuracy in 3D occupancy maps

Effective in real-life CT scan datasets

Abstract

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.