For two problems in Gaussian mixture probability hypothesis density SLAM(GMPHD-SLAM) algorithm of low estimation accuracy and high computational cost, the GMPHD-SLAM algorithm based on unscented transform, called unscented-GMPHD-SLAM, is proposed. The main contribution lies that: the unscented Kalman filter is used in the calculation of particle’s weight and PHD update process, which improves the performance of the algorithm; the updated Gaussian components are classified based on the sensor’s field of view(FoV) , which reduces the computational cost. The proposed algorithm is compared with the traditional PHD-SLAM algorithm. The results show that the proposed algorithm is effective in accuracy improvement and reduction of computational cost.