In this paper we address an important issue in human-robot interaction, that of accurately deriving pointing information from a corresponding gesture. Based on the fact that in most applications it is the pointed object rather than the actual pointing direction which is important, we formulate a novel approach which takes into account prior information about the location of possible pointed targets. To decide about the pointed object, the proposed approach uses the Dempster-Shafer theory of evidence to fuse information from two different input streams: head pose, estimated by visually tracking the off-plane rotations of the face, and hand pointing orientation. Detailed experimental results are presented that validate the effectiveness of the method in realistic application setups.

In this paper we present a novel approach for hand gesture recognition. The proposed system utilizes upper body part tracking in a 9-dimensional configuration space and two Multi-Layer Perceptron/Radial Basis Function (MLP/RBF) neural network classifiers, one for each arm. Classification is achieved by buffering the trajectory of each arm and feeding it to the MLP Neural Network which is trained to recognize between five gesturing states. The RBF neural network is trained as a predictor for the future gesturing state of the system. By feeding the output of the RBF back to the MLP classifier, we achieve temporal consistency and robustness to the classification results. The proposed approach has been assessed using several video sequences and the results obtained are presented in this paper.

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.

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 avoid 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.

Tracking of the upper human body is one of the most interesting and challenging research fields in computer vision and comprises an important component used in gesture recognition applications. In this paper a probabilistic approach towards arm and hand tracking is presented. We propose the use of a kinematics model together with a segmentation of the parameter space to cope with the space dimensionality problem. Moreover, the combination of particle filters with hidden Markov models enables the simultaneous tracking of several hypotheses for the body orientation and the configuration of each of the arms.

This paper describes an off-line, iterative algorithm for simultaneous localization and mapping within large indoor environments. The proposed approach is based on the idea of separately tracking multiple robot pose hypotheses that are generated each time the robot closes a loop by revisiting an already mapped area. Each tracked pose hypothesis corresponds to a separate possible robot path and maintains a separate map. Loop identification is inherently pursued by a hybrid localization algorithm with global localization capabilities. During the first step of the algorithm, all recorded sensor information is sequentially processed, in order to create a set of possible robot paths and their corresponding maps. After all sensor input is processed, the history of all tracked hypotheses is validated in order to select the most probable robot path and the corresponding map. The second step of the algorithm utilizes an EM-like iterative procedure in order to close the loops identified by the first step, compute a correct robot pose history and produce the final map. Experimental results demonstrate that the proposed algorithm facilitates computation of correct maps regardless of the number of loops in the robot's path

In this paper we propose an offline feature mapping algorithm capable of identifying and correctly closing multiple loops in cyclic environments. The proposed algorithm iteratively alternates between a Kalman smoother based localization step and a map features recalculation step. The identification of loops is done during the localization step by a hybrid localization algorithm that generates and tracks hypotheses generated each time the robot visits an already mapped area. The main contribution of this paper lies on the ability of the proposed algorithm to exploit information contained within the hypotheses histories in order to calculate correct maps, regardless of the complexity of the environment and the number of loops in the robot's path.

In this paper we focus on one of the most important issues for autonomous mobile robots: their ability to localize themselves safely and reliably within their environments. We propose a probabilistic framework for modelling the robot's state and sensory information based on a switching state-space model. The proposed framework generalizes two of the most successful probabilistic model families currently used for this purpose: the Kalman filter linear models and hidden Markov models. The proposed model combines the advantages of both models, relaxing at the same time inherent assumptions made individually in each of these existing models.

In this paper we deal with one of the fundamental problems for mobile robots, namely their ability to produce accurate representations of their environment. We propose an off-line mapping algorithm that iteratively alternates between a Kalman smoother based localization step and a map features recalculation step. Moreover, a hybrid algorithm with global localization capabilities is employed in the first step, enabling correct identification of already mapped areas, and, thus, ensuring map correctness in cyclic environments.

Abstract Knowledge of the position and orientation of 3D planes that exist in a scene is very important for many machine vision-based tasks, such as navigation, self-localization and docking. In this paper we propose two iterative methods for robustly computing the plane parameters out of given image sequences. Both methods include the (limited) ability to determine unknown parameters of the 3D-camera motion.