Perceptual Anchoring for Gaze-Tracking Wearables and Robot-Mounted Sensors

For persons with motor disabilities, robots have a great potential to improve the quality of daily interactions. A fully-fledged solution requires addressing many different challenges. Gaze-tracking wearables provides a new input modality for such solutions. In this paper we address the challenge of building and maintaining a world model using robot and gaze-tracking sensing data. We construct a world modeling pipeline consisting of a data association step and an anchoring step. In the data association step we compare four different methods. The results show that three possible data association methods outperform a geometric baseline. However, the results have potential for improvement, and thus future work to enhance the performance of the pipeline is needed. To guide future work, we have investigated potential factors which affect the performance of both the data association and the anchoring process, and found that the positions of detected objects have a significant impact.