Localization and Angle Estimation of Capsule Robots in Ultrasound Images Using Spatially Adaptive Gaussian Distribution

Abstract

The ingestible capsule robot has shown significant promise for non-invasive diagnosis and drug delivery within the gastrointestinal tract. Locomotion control of the capsule robot requires real-time, accurate localization and angle estimation from ultrasound images. This paper introduces a novel CNN model with spatially adaptive Gaussian distribution, which can simultaneously finish the localization and angle estimation of capsule robots in US images. Specifically, we propose a robot-adaptive label assignment strategy using an elliptical Gaussian to better suit the shape and orientation of the capsule robot. Additionally, we employ a novel bounding box representation for capsule robot localization to encode predictions from the CNN model. Experimental results demonstrate that our method finishes real-time localization and angle estimation of capsule robots at approximately 48 frames per second. Moreover, our approach achieves a small localization error of 0.16 mm and a high angle estimation accuracy of 99.85%. The method developed, utilizing spatially adaptive Gaussian distribution, holds practical significance in achieving accurate and real-time position and angle feedback for the locomotion control of capsule robots.