Adaptive C-SLAM Algorithm in Dynamic Environment

In the last two decades, many researchers have focused on the problem of automation of vehicles, and many research has been devoted to solving the challenges posed by this area. One of the important aspects in this area is the problem of localizing the vehicle and mapping the environment simultaneously in an unknown environment, which is briefly referred to as SLAM. So far, many methods have been proposed to solve this problem, but few of these researches have been implemented on the platform of collaborative robots. In this paper, SLAM problem is extended to multi robot platform by employing extended kalman filter. Due to lack of knowledge about the measurement noise covariance, the elements of this matrix adapted according to the actual data received from the sensor by employing particle swarm optimization technique. Then, to solve this problem in the dynamic environment, probability hypothesis density filter is used to track the dynamic objects in the field of view of sensors. Finally, the performance of the algorithm is evaluated in a MATLAB environment.