Low cost imaging and positioning sensors are opening new frontiers for applications in near real-time Photogrammetry. Omnidirectional cameras acquiring images with 360° coverage, when combined with information coming from GNSS (Global Navigation Satellite Systems) and IMU (Inertial Measurement Unit), can efficiently estimate orientation and object space structure. However, several challenges remain in the use of low-cost sensors and image observations acquired by sensors with non-perspective inner geometry. The accuracy of the measurement using low-cost sensors is affected by different sources of errors and sensor stability. Microelectromechanical systems (MEMS) present a large gap between predicted and actual accuracy. This work presents a study on the performance of an integrated sensor orientation approach to estimate sensor orientation and 3D sparse point cloud, using an incremental bundle adjustment strategy and data coming from a low-cost portable mobile terrestrial system composed by off-theshelf navigation systems and a poly-dioptric system (Ricoh Theta S). Experiments were performed in an outdoor area (sidewalk), achieving a trajectory positional accuracy of 0.33 m and a meter level 3D reconstruction.