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In communication between humans, gestures are often preferred orcomplementary to verbal expression since the former offers better spatialreferral. Finger pointing gesture conveys vital information regarding somepoint of interest in the environment. In human-robot interaction, a user caneasily direct a robot to a target location, for example, in search and rescueor factory assistance. State-of-the-art approaches for visual pointingestimation often rely on depth cameras, are limited to indoor environments andprovide discrete predictions between limited targets. In this paper, we explorethe learning of models for robots to understand pointing directives in variousindoor and outdoor environments solely based on a single RGB camera. A novelframework is proposed which includes a designated model termed PointingNet.PointingNet recognizes the occurrence of pointing followed by approximating theposition and direction of the index finger. The model relies on a novelsegmentation model for masking any lifted arm. While state-of-the-art humanpose estimation models provide poor pointing angle estimation accuracy of28deg, PointingNet exhibits mean accuracy of less than 2deg. With the pointinginformation, the target is computed followed by planning and motion of therobot. The framework is evaluated on two robotic systems yielding accuratetarget reaching.

Recognition and Estimation of Human Finger Pointing with an RGB Camera for Robot Directive