A Rapid Dual Feature Tracking Method for Medical Equipments Assembly and Disassembly in Markerless Augmented Reality

Abstract

Markerless Augmented Reality (MAR) is a superior technology that is currently used by the medical device assembler with aid in design, assembly, disassembly and maintenance operations. The medical assembler assembles the medical equipment based on the doctors requirement, they also maintains quality and sanitation of the equipment. The major research challenges in MAR are as follows: establish automatic registration parts, find and track the orientation of parts, and lack of depth and visual features. This work proposes a rapid dual feature tracking method i.e., combination of Visual Simultaneous Localization and Mapping (SLAM) and Matched Pairs Selection (MAPSEL). The main idea of this work is to attain high tracking accuracy using the combined method. To get a good depth image map, a Graph-Based Joint Bilateral with Sharpening Filter (GRB-JBF with SF) is proposed since depth images are noisy due to the dynamic change of environmental factors that affects tracking accuracy. Then, the best feature points are obtained for matching using Oriented Fast and Rotated Brief (ORB) as a feature detector, Fast Retina Key point with Histogram of Gradients (FREAK-HoG) as a feature descriptor, and Feature Matching using Rajsk’s distance. Finally, the virtual object is rendered based on 3D affine and projection transformation. This work computes the performance in terms of tracking accuracy, tracking time, and rotation error for different distances using MATLAB R2017b. From the observed results, it is perceived that the proposed method attained the least position error value about 0.1 cm to 0.3 cm. Also, rotation error is observed as minimal between 2.40 (Deg) to 3.10 and its average scale is observed as 2.7140. Further, the proposed combination consumes less time against frames compared with other combinations and obtained a higher tracking accuracy of about 95.14% for 180 tracked points. The witnessed outcomes from the proposed scheme display superior performance compared with existing methods.