INS/GNSS integration scheme can overcome the shortcoming of GNSS or INS alone to provide superior performance. The position and velocity from GNSS is an excellent external aid to update the INS with improving its long-term accuracy. AKF is based on the maximum likelihood criterion for choosing the most appropriate weight and thus to adjust Kalman gain factors online. The conventional EKF implementation suffers uncertain results while the update measurement covariance matrix R does not meet the case. The primary advantage of AKF is that the filter has less relationship with the priori statistical information because R varies with time. The innovation sequence is used to derive the measurement weights through the measurement covariance matrices, innovation-based adaptive estimation (IAE) in this study. There are two non-holonomic constraints (NHC) available for land vehicle navigation. Land vehicles will not jump off or slid on the ground under normal condition. Using these constraints, the velocity of the vehicle in the plane perpendicular to the forward direction is almost zero. EKF and AKF based tightly-coupled scheme with NHC are implemented in the study. To validate the performance of EKF and AKF based tightly-coupled INS/GNSS integration scheme with NHC, field scenarios were conducted in the downtown area of Tainan city. The data fusion of INS/GNSS/NHC can be used as stand-alone positioning tool during GNSS outages of over 1 minute. The preliminary results presented in this study illustrated that AKF based tightly-coupled INS/GNSS integration scheme can provide more stable solutions. Generally speaking, the improvement ratio of 3D positioning of proposed algorithm reach 40% compared to EKF based tightly-coupled INS/GNSS integration scheme.