Paper Info
Title
Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance
Abstract
Our novel skin-feature visual-tracking algorithm enables anatomic vSLAM and (by extension) localization of clinical tools relative to the patient's body. Tracking naturally occurring features is challenging due to patient uniqueness, deformability, and lack of an accurate a-priori 3D geometric model. Our method (i) tracks skin features in a smartphone-camera video sequence, (ii) performs anatomic Simultaneous Localization And Mapping (SLAM) of camera motion relative to the patient's 3D skin surface, and (iii) utilizes existing visual methods to track clinical tool(s) relative to the patient's reconstructed 3D skin surface. (We demonstrate tracking of a simulated ultrasound probe relative to the patient by using an Apriltag visual fiducial). Our skin-feature tracking method utilizes the Fourier-Mellin Transform for robust performance, which we incorporated and extend an existing Phase Only Correlation (POC) based algorithm to be suitable for our application of free-hand smartphone video, wherein the distance of the camera fluctuates relative to the patient. Our SLAM approach further utilizes Structure from Motion and Bundle Adjustment to achieve an accurate 3D model of the human body with minimal drift-error in camera trajectory. We believe this to be the first freehand smartphone-camera tracking of natural skin features for anatomic tracking of surgical tools, ultrasound probe, etc.
Year
DOI
Venue
2021
10.1109/ICRA48506.2021.9561616
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
DocType
Volume
Issue
Conference
2021
1
ISSN
Citations 
PageRank 
1050-4729
0
0.34
References 
Authors
5
2
Name
Order
Citations
PageRank
Chun-Yin Huang100.34
J.M. Galeotti27513.90