###### Abstract  
The present article describes a new and efficient method of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) assisted terrestrial Structure-from-Motion (SfM) photogrammetry without the need for Ground Control Points (GCPs). The system only requires a simple frame that mechanically connects a RTK GNSS antenna to the camera. The system is low cost, easy to transport, and offers high autonomy. Furthermore, not requiring GCPs enables saving time during the in situ acquisition and during data processing. The method is tested for coastal cliff monitoring, using both a Reflex camera and a Smartphone camera. The quality of the reconstructions is assessed by comparison to a synchronous Terrestrial Laser Scanner (TLS) acquisition. The results are highly satisfying with a mean error of 0.3 cm and a standard deviation of 4.7 cm obtained with the Nikon D800 Reflex camera and, respectively, a mean error of 0.2 cm and a standard deviation of 3.8 cm obtained with the Huawei Y5 Smartphone camera. This method will be particularly interesting when simplicity, portability, and autonomy are desirable. In the future, it would be transposable to participatory science programs, while using an open RTK GNSS network. S fM photogrammetry; direct georeferencing; without GCP; coastal monitoring; cliff Article  
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since they also require a tripod, batteries, reflective targets, and a Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) or a total station to measure the targets' position [8]. Concerning photogrammetric methods, the development of Structure-from-Motion (SfM) techniques has made data acquisition and processing easier in comparison with classical digital photogrammetry [10; 11]. Indeed, being based on a Computer Vision approach, the SfM method automatically and simultaneously solves the camera interior and exterior parameters by self-calibration and scene geometry, using image matching and bundle block adjustment process. Similarly, the generalization of the SIFT (Scale Invariant Feature Transform) algorithm [12] has changed the approach of tiepoint detection, enabling scale- and rotation-invariant image matching and, therefore, making the acquisition more flexible. The bundle adjustment step in SfM photogrammetry is a multiview adjustment (involving all images simultaneously) based on the collinearity equations. During this step, several variables and parameters of the collinearity equations are computed or optimized: (i) the three-dimensional (3D) position of each tiepoint, (ii) the external camera parameters (position and orientation), and (iii) the internal camera parameters. For many practical applications, the parameters are optimized by error minimization on Ground Control Points (GCPs), whose coordinates in real world are known or independently measured. Using this approach, GCPs usually play a critical role in affecting the quality of the DEM and orthophotograph reconstruction. Reciprocally, the method is very dependent on GCP characteristics such as GCP type, number, distribution and quality of localization [13; 14; 15], which can be operator-dependent or specific to a study. Consequently, previous research has looked at the optimization of GCP networks (e.g., by varying GCP density and spatial distribution [13; 16]) or by optimizing the flight scenario and the imaging configuration to limit the impact of GCPs [17; 18; 19]. Nevertheless, in practice, achieving a satisfying GCP network over the study area, measuring their position and pointing them on the photographs are time-consuming steps, which are sometimes impracticable [20]. A different approach to avoid or limit the use of GCPs during bundle adjustment is direct georeferencing. By fixing the values of certain parameters in the collinearity equations, some of the ambiguities are removed.