Localization and Navigation Omni-directional Robots based on Sensors Fusion and Particle Filter

One of the main challenges in autonomous mobile robots is robust and accurate navigation of a robot in its environment. Obviously, this is conceivable when the precise position of the robot in the environment is needed to get by a control algorithm before navigation. Due to the lack of coverages for using one sensor in environmental conditions, multi-sensor data fusion is one of the successful solutions to overcome these problems. This paper presents an efficient localization and navigation of an industrial robot using integration of multi sensors. To do this, the relative position of the robot is obtained by passing normalized data of laser scanner, compass and encoder sensors to a Kalman filter. Due to relative and accumulated errors in the Kalman filter, exact position is obtained by applying a particle filter on environment's map. The proposed method has been run on a real industrial robot in an industrial environment according to RoboCup rules. The experimental results achieved in mean error
$x, y$
, θ was 0.98 and 0.93 and 0.09 cm / m respectively. These results show that have much lower error than the other test results of each of the sensors used alone.