Nowadays, Mobile Mapping Systems (MMSs) are used to acquire spatial information such as road paths, images and point clouds that can be directly georeferenced using an integrated GNSS/INS system. Thanks to the high performance and the quick time to have final products (geo-information), the use of MMSs will be extended to new application fields. The Politecnico di Torino research team has developed a Low Cost Mobile Mapping System (LCMMS), where only low cost sensors are involved. The system is equipped with webcams, a MEMS IMU and up to three GNSS receivers. The main problem of this low cost system is when some GNSS outages occur. In this case, the IMU can estimate the trajectory and the attitude of the vehicle, adopting particular integration algorithms, such as the loosely or the tightly coupled. These methods allow improving the IMU performance because the gyroscope and accelerometer drifts are reduced. Anyway, these results get gradually worse for long GNSS outages (more than 30 s): driving in long tunnels or in dense urban areas. In these conditions, the information achieved by the acquired images can be used in order to improve the positioning by means of a photogrammetric approach, exploiting the overlap between adjacent images. For this reason, an automatic feature extraction and matching has been performed. Then, homologous points recognition in image sequences have been obtained, in order to obtain a bundle block adjustment. The solutions of navigation are refined considering the contribution given by the bundle block adjustment. In this paper a detailed description of this integrated system is presented; then, the first tests and the achieved results are shown in order to evaluate the goodness of the proposed approach. In particular, different environmental conditions, outages, kinds of trajectory are considered. Finally, these results are compared with the reference dataset to evaluate the accuracy of our solutions.