In a mobile mapping system, the integration of Inertial Navigation System (INS) and Global Positioning System (GPS) is widely applied for determining position and orientation. The Kalman filter or Extended Kalman Filter (EKF) is popularly used for data fusion estimation. In such those estimation strategies, linearization and assuming Gaussian distribution are utilized. However, the fact that the system model and measurement model in INS/GPS integration are originally non-linear and the noise arising during operation may be non-Gaussian distribution. These characteristics may leads to the low performance of the system utilizing KF or EKF in case of highly non-linear model and non-Gaussian noises. This paper investigates on some of non-linear, non-Gaussian estimation strategies in order to improve the performance of the system.