Improving the Navigation of Indoor Mobile Robots Using Kalman Filter

For a secure navigation of indoor mobile robots, several tasks have to be achieved to ensure the ability of these robots to implement manipulation tasks successfully. One of these challenges is the ability of robots to specify its location into the work environment under different conditions. Several localization methods have been implemented for different robotics' applications. The key success of these methods is selecting a suitable sensory system and an appropriate localization algorithm which takes into consideration the properties of the application and work area. This paper will focus on developing a robust navigation system for indoor mobile robots to be able to move in narrow corridors and crowded areas under different work circumstances. The proposed system will be based on a Kalman filter for rejecting the false measurements of the localization sensor, and providing estimation for the location under these false values. The experimental results prove the validity of the proposed filter in detecting and compensating the false measurements of the Stargazer sensor, and improving the localization of robots.