An approach for tracking of mobile robot with vision sensor

In mobile robots tracking is an important requirement for real time path planning and obstacle avoidance. Current work proposes an innovative approach which combines feature based object detection, KLT Algorithm based tracking method and Kalman filter based de-noising technique for mobile robot working in real time environment. In the detection phase, the mobile robot is detected using Viola Jones algorithm which extracts detectable feature. Then the position of mobile robot is computed with homography constraints and a region of interest window was set up to accommodate the mobile robot. In the tracking phase, the region of interest window was dealt with using KLT algorithm. The proposed method is of special practical importance in case of specified path tracking as the size of mobile robot image is usually small relative to the captured image of the environment. Thus the analysis of captured image of environment become unnecessary for tracking and thereby reduce computational load. Experiments show that the proposed approach accurately detect and track the mobile robot with error percentage ranging from 0.5% to 8% in different parts of the specified path.