This paper addresses the idea of establishing symbolic communication between mobile robots through gesturing. Humans communicate using body language and gestures in addition to other linguistic modalities like prosody and text or dialog structure. This research aims to develop a pointing gesture detection system for robot to robot communication scenarios to grant robots an ability to convey object identity information without global localization of the agents. The detection is based on RGB-D and a NAO humanoid robot is used as the pointing agent in the experiments. The presented algorithms are based on PCL library. The results indicate that real-time detection of pointing gesture can be performed with little information about the embodiment of the pointing agent and that an observing agent can use the gesture detection to perform actions on the pointed targets.
In this paper we present a novel methodology for detection and tracking of facial features like eyes, nose and mouth in image sequences. The proposed methodology is intended to support natural interaction with autonomously navigating robots that guide visitors in museums and exhibition centers and, more specifically, to provide input for the analysis of facial expressions that humans utilize while engaged in various conversational states. For face and facial feature region detection and tracking, we propose a methodology that combines appearance-based and feature-based methods for recognition and tracking, respectively. For the stage of face tracking the introduced method is based on Least Squares Matching (LSM), a matching technique able to model effectively radiometric and geometric differences between image patches in different images. Thus, compared with previous research, the LSM approach can overcome the problems of variable scene illumination and head in-plane rotation. Another significant characteristic of the proposed approach is that tracking is performed on the image plane only wherever laser range information suggests so. The increased computational efficiency meets the real time demands of human-robot interaction applications and hence facilitates the development of relevant systems.
Abstract-This paper addresses the problem of real-time moving-object detection, classification and tracking in populated and dynamic environments. In this scenario, a mobile robot uses 2D laser range data to recognize, track and navigate moving targets. Most previous approaches either rely on pre-defined data features or off-line training of a classifier for specific data sets, thus eliminating the possibility to detect and track different-shaped moving objects. We propose a novel and adaptive technique where potential moving objects are classified and learned in real-time using a Fuzzy ART neural network algorithm. Experimental results indicate that our method can effectively distinguish and track moving targets in cluttered indoor environments, while at the same time learning their shape.
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