Self-localization using fixations as landmarks

Abstract

Self-localization is the process of knowing your position and location relative to your surroundings. This research integrated artificial intelligence techniques into a custom-built portable eye tracker for the purpose of automating the process of determining indoor self-localization. Participants wore the eye tracker and walked a series of corridors while a video of the scene was recorded along with fixation locations. Patches of the scene video without fixation information were used to train the classifier by creating feature maps of the corridors. For testing the classifier, fixation locations in the scene were extracted and used to determine the location of the participant. Scene patches surrounding fixations were used for the classification instead of objects in the environment. This eliminated the need for complex computer vision object recognition algorithms and made scene classification less dependent upon objects and their placement in the environment. This allowed for a sparse representation of the scene since image processing to detect and recognize objects was not necessary to determine location. Experimentally, image patches surrounding fixations were found to be a highly reliable indicator of location, as compared to random image patches, non-fixated salient image patches, or other non-salient scene locations. In some cases, only a single fixation was needed to accurately identify the correct location of the participant. To the best of our knowledge, this technique has not been used before for determining human self-localization in either indoor or outdoor settings.