SLAM-based localization of 3D gaze using a mobile eye tracker

Abstract

Past work in eye tracking has focused on estimating gaze targets in two dimensions (2D), e.g. on a computer screen or scene camera image. Three-dimensional (3D) gaze estimates would be extremely useful when humans are mobile and interacting with the real 3D environment. We describe a system for estimating the 3D locations of gaze using a mobile eye tracker. The system integrates estimates of the user's gaze vector from a mobile eye tracker, estimates of the eye tracker pose from a visual-inertial simultaneous localization and mapping (SLAM) algorithm, a 3D point cloud map of the environment from a RGB-D sensor. Experimental results indicate that our system produces accurate estimates of 3D gaze over a much larger range than remote eye trackers. Our system will enable applications, such as the analysis of 3D human attention and more anticipative human robot interfaces.