Semantic-Coupled Perception Framework for Autonomous Laparoscope Control
Enduo Zhao, Fangzhou Li, Yujie Zhu, Sicheng Lu, Fang Chen, Longfei Ma, Hongen Liao
原文摘要
In minimally invasive surgery (MIS), maintaining an appropriate field of view (FoV) is essential for surgical safety and efficiency. However, conventional autonomous laparoscope control methods often treat visual tracking and surgical phase recognition independently, limiting robustness under occlusion, entity overlap, and camera motion. This paper proposes a closed-loop autonomous laparoscope control framework that couples real-time surgical phase recognition with camera-motion-compensated visual perception. By exploiting phase-dependent relationships among surgical entities, the framework improves target state estimation under challenging visual conditions, supporting task-aware autonomous FoV control. Benchmark and phantom experiments evaluate the framework in surgical phase recognition, target tracking under occlusion and entity overlap, and autonomous FoV control, showing improved target visibility and task execution compared with fixed-FoV control. These results highlight the potential of incorporating task-level semantics into visual state estimation for autonomous laparoscope guidance.
cs.RO · eess.SY