On an improved SLAM algorithm in indoor environment

A new simultaneous localization and mapping (SLAM) algorithm based on the square root unscented Kalman filter (SRUKF) is proposed for indoor environments. This algorithm uses square root unscented particle filter for estimating the robot states in every iteration, meanwhile, introduces SRUKF to localize the estimated landmarks, and then updates the robot states and landmark information. The proposed algorithm is combined with the robot motion model and observation model of infrared tag in simulation and experiment, and the results show that the algorithm improves the accuracy and stability of the estimated robot state and landmarks in SLAM.