Implementing an indoor navigation system requires alternative techniques to the GPS. One solution is vision-based localization which takes advantage of visual landmarks and a camera to read the environment and determine positioning. Three computer vision algorithms used for pose estimation are tested and evaluated in this project in order to determine their viability in an indoor navigation system. Two algorithms, SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features), take advantage of the natural features in an image, whereas the third algorithm, ArUco, uses a manufactured marker. The evaluation displayed certain advantages for all solutions, however with the goal of using it for a navigation system ArUco was the superior solution as it performed well for key criteria, mainly computational performance and range of detection. An indoor navigation system for Android devices was developed using ArUco marker tracking for positioning and augmented reality for pro- jecting the route. The application was able to successfully fulfill its goal of providing route guidance to a specific target location.