ArthroSLAM: Multi-sensor robust visual localization for minimally invasive orthopedic surgery

Minimally invasive arthroscopic surgery is a very challenging procedure that requires the manipulation of instruments in limited intraarticular space using distorted and sometimes uninformative images. Localizing the arthroscope reliably and at all times w.r.t. surrounding tissue is of fundamental importance to prevent unintended injury to patients. However, even highly-trained surgeons can struggle to localize the arthroscope using poor image feedback. In this paper, we propose and demonstrate for the first time a visual Simultaneous Localization and Mapping (SLAM) system, termed ArthroSLAM, capable of robustly and reliably localizing an arthroscope inside a human knee joint. The proposed system fuses the information obtained from the arthroscope, an external camera mounted on an arthroscope holder, and the odometry of a robotic arm manipulating the scope, in an Extended Kalman Filter framework. Also for the first time, we implement five alternative strategies for localization and compare them to our method in a realistic setup with a human cadaver knee joint. ArthroSLAM is shown to outperform the alternative strategies under various challenging conditions, localizing reliably and at all times with a mean Relative Pose Error of up to 1.4mm and 0.7°. Additional experiments conducted with degraded odometry data also validate the robustness of the method. An initial evaluation of the sparse map of a knee section computed by our method exhibits good morphological agreement. All results suggest that ArthroSLAM is a viable component for the robotic orthopedic surgical assistant of the future.