Recently, there has been an increase in interest in the use of wide-angle cameras in multi-image matching for the indoor 3D mapping and indoor localization. The demand for rapid 3D models of spaces in unknown environments is increasingly observed. That is particularly important when modelling unknown objects to conduct reconnaissance or building intervention after a disaster. In this case, developing a 3D model using a robot equipped with a system of synchronized stereo cameras with a short length longitudinal base is extremely desirable. In these studies, we present the approach to indoor location based on a 3D model developed from a dense point cloud with multi-image matching technique. As part of the research, an imaging system was developed, and an algorithm that converts images of selected objects to 3D model was implemented. The research presents the method of determining the object position based on the calculation of reference points’ disparity based on the Sum of Absolute Differences (SAD). Next, a dense point cloud was generated based on the method of mutual image matching using Structure from Motion (SfM) algorithms. The resulting dense cloud of points had a resolution of 0.05 m. Based on the developed algorithm, a method for generating a quick model of the environment based on multi-image matching and disparity maps was presented. The obtained test results confirmed the possibilities of using the developed methodology for the needs of rapid reconnaissance of the environment to determine the distance, location and size of objects of interest. The mapping accuracy is at a decimeter level, and the possibility of geolocation of objects can be performed with an accuracy of ± 0.15 m. Based on the obtained test results, the potential of using miniature, portable mobile image-based mapping systems has been demonstrated to identify and model inaccessible rooms. Further work will be focused on the improvement of the geometric image quality and to increase the accuracy of the calibration.