A novel approach for positioning using smartphones and image processing techniques is developed. Using structure from motion, 3D reconstructions of given tracks are created and stored as sparse point clouds. Query images are matched later to these 3D models. High computational costs of image matching and limited storage require compressing point clouds without loss of positioning performance. In this work, localization is improved and memory and storage requirements are minimized. We assumed that the computational speed and, at the same time, storage requirements benefit from reducing the number of points with appropriate outlier detection. In particular, our hypothesis was that positioning accuracy is maintained while reducing outliers in a reconstructed model. To evaluate the hypothesis, three methods were compared - (i) density-based (Sotoodeh, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI-5, 2006), (ii) connectivity-based (Wang et al. Comput Graph Forum 32(5):207–10, 2013), and (iii) our distance-based approach. In tenfold cross-validation, applied to a pre-reconstructed reference 3D model, localization accuracy was measured. In each new model, the positions of test images were identified and compared to the according positions in the reference model. We observed that outlier removal has a positive impact on matching run-time and storage requirements, while there are no significant differences in the localization error within the methods. That confirmed our initial hypothesis and allows mobile application of image-based positioning.