Real-Time Human Finger Pointing Recognition and Estimation for Robot Directives Using a Single Web-Camera

Gestures play a pivotal role in human communication, often serving as a preferred or complementary medium to verbal expression due to their superior spatial reference capabilities. A finger-pointing gesture conveys vital information regarding some point of interest in the environment. In Human-Robot Interaction (HRI), users can easily direct robots to target locations, facilitating tasks in diverse domains such as search and rescue or factory assistance. State-of-the-art approaches for visual pointing estimation often rely on depth cameras, are limited to indoor environments, and provide discrete predictions between limited targets. In this paper, we explore the development of models that enable robots to understand pointing directives from humans using a single web camera, even in diverse indoor and outdoor environments. A novel perception framework is proposed which includes a designated data-based model termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PointingNet</i>. PointingNet recognizes the occurrence of pointing through classification followed by approximating the position and direction of the index finger with an advanced regression model. The model relies on a novel segmentation model for masking any lifted arm. While state-of-the-art human pose estimation models provide poor pointing angle estimation error of 28°, PointingNet exhibits a mean error of less than 2°. With the pointing information, the target location is computed, followed by robot motion planning and execution. The framework is evaluated on two robotic systems, demonstrating accurate target reaching.