A Comparative Analysis of Visual Localization Algorithms in Indoor Navigation

Abstract

Localization can be defined as the process of determining the position and orientation of an entity within an environment, that would enable it to navigate and carry out tasks effectively. It is of fundamental importance for a wide range of areas such as robotics, medicine, indoor and outdoor navigation, autonomous vehicles, etc. Localization problems might be solved either with hardware or software designs. However, considering the challenging environments that would need a localization process, hardware designs might be complicated to apply to this problem. Indoor navigation can be shown as an example of these challenging environments since classical positioning methods cannot be used in such places. In this case, visual localization might be a solution since it requires either monocular or stereo images taken through the path. It would be a time-saving and cost-effective way to determine the locations that images were taken, thus the path that a robot or medical instrument, etc. take along the way. In this case, it is important the determine the performances of different localization algorithms. In this study, the performance of two different localization algorithms in indoor navigation was tested. In this context, Visual odometry and EKF SLAM algorithms were used to determine the camera trajectory utilizing the images that were taken in a straight corridor with a smartphone camera. To determine the accuracy of each method, the distances between each image-taking point were measured and compared with the distances obtained from the algorithm. Thus, root mean square error values were determined by each method. The precisions of each method were also given based on the fact that the distance between each image-taking point was equal. Therefore, the usage of both algorithms in indoor navigation was discussed.