An integrative approach for tracking of mobile robot with vision sensor

Abstract

Current work addresses an experimental approach which incorporates feature-based object detection, KLT algorithm-based tracking method and Kalman filter-based de-noising technique in a real-time environment. In the detection phase, the mobile robot is detected using Viola-Jones algorithm which extracts detectable features. Then the position of the mobile robot is computed with homography constraints and a region of interest window is set up to accommodate the mobile robot. In the tracking phase, the region of interest window is dealt with using KLT algorithm. The proposed method is of practical importance when the mobile robot is tracked while moving on a predetermined (specified) path as the size of the image of the mobile robot is small relative to the captured image of the environment. Thus the analysis of captured image of environment becomes unnecessary for tracking and thereby the approach reduces computational load. The proposed approach accurately detects and tracks the mobile robot with error percentage ranging from 0.5% to 10% in different parts of the specified path.