Gaze Selection for Enhanced Visual Odometry During Navigation

We present an approach to enhancing visual odometry and Simultaneous Localization and Mapping (SLAM) in the context of robot navigation by actively modulating the gaze direction to enhance the quality of the odometric estimates that are returned. We focus on two quality factors: i) stability of the visual features, and ii) consistency of the visual features with respect to robot motion and the associated correspondence between frames. We assume that local texture measures are associated with underlying scene content and thus with the quality of the visual features for the associated region of the scene. Based on this assumption, we train a machine-learning system to score different regions of an image based on their texture and then guide the robot's gaze toward high scoring image regions. Our work is targeted towards motion estimation and SLAM for small, lightweight, and autonomous air vehicles where computational resources are constrained in weight, size, and power. However, we believe that our work is also applicable to other types of robotic systems. Our experimental validation consists of simulations, constrained tests, and outdoor flight experiments on an unmanned aerial vehicle. We find that modulating gaze direction can improve localization accuracy by up to 62 percent.