A Vision-based travelled Distance estimation Algorithm in an indoor Environment using a Mobile robot

Autonomous navigation for a mobile robot still remains as a challenging area to be explored. In an indoor environment, while GPS is unavailable and wheel encoder suffer from error accumulation due to wheel slips, vision-based travelled distance estimation can be considered as an alternative approach for more accurate measurements. This study presents a new algorithm to estimate travelled distance of the mobile robot in an indoor environment. Using a downward looking camera, features points are detected from the floor texture and tracked with the Lucas-Kanade optical flow technique. The measurement accuracy of this algorithm will be put through several experiments on real scenarios which involve comparing the proposed techniques, running the algorithm on different type of indoor surface at different speed and different trajectories.