An improved Gaussian mixture PHD SLAM algorithm

作者：Yan, De-Li; Song, Yong-Duan; Song, Yu; Kang, Yi-Fei

来源：Control and Decision, 2014, 29(11): 1959-1965.

DOI：10.13195/j.kzyjc.2013.1222

• Accuracy Improvement
• Computational costs
• Gaussian components
• Gaussian mixture phd
• Gaussian mixture probability hypothesis density
• SLAM
• Unscented Kalman Filter
• Unscented transform

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.

• 出版日期2014
• 单位石家庄铁道大学