Retour au site du LAAS-CNRS

Mots-Clés / Keywords

Abstract

This paper is about robust filtering/prediction of nonlinear discrete-time systems with rational dependence on the state or uncertainty vectors. The problem is dealt with in a set-membership context, in that the system initial condition, the uncertainties, as well as the noises affecting the dynamics and the measurements are unknown but bounded. A new recursive approach to the prediction of confidence ellipsoids enclosing the state vector at each sampling time is proposed, based on the reformulation of the system model as a Recursive Algebraic Representation. The solution is expressed as a convex optimization problem under Linear Matrix Inequality (LMI) constraints. The method is assessed on some examples.

Mots-Clés / Keywords

Abstract

In this article we present a two ambiance video camera system dedicated to markerless human motion capture. We introduce a new particle filter algorithm which entails an importance function enabling auto-(re)initialisation, and takes account of the global curvature of the likelihood so as to guide the search along poorly observable directions of the state space. The system robustness is improved by fusing different visual cues. Performances are nearly real time.

Mots-Clés / Keywords

Mots-Clés / Keywords

Abstract

This paper proposes a convex approach to regional stability and 2-gain analysis and control synthesis for a class of nonlinear systems subject to bounded disturbance signals, where the system matrices are allowed to be rational functions of the state and uncertain parameters. To derive sufficient conditions for analysing input-to-output properties, we consider polynomial Lyapunov functions of the state and uncertain parameters (assumed to be bounded) and a differential-algebraic representation of the nonlinear system. The analysis conditions are written in terms of linear matrix inequalities determining a bound on the 2-gain of the input-to-output operator for a class of (bounded) admissible disturbance signals. Through a suitable parametrization involving the Lyapunov and control matrices, we also propose a linear (full-order) output feedback controller with a guaranteed bound on the 2-gain. Numerical examples are used to illustrate the proposed approach.

Mots-Clés / Keywords

Abstract

This paper deals with visual recognition and tracking of people and gestures from a camera mounted on a tour-guide robot in a human, cluttered, environment. The particle filtering framework enables the fusion of visual cues, both into an importance function from which the particles are sampled, and into a measurement model used for weights definition. The multi-cues associations prove to be more robust than any of the cues individually. For the purpose of gestures recognition, a tracker is proposed which handles multiple hand configurations templates. Finally, implementation and experiments on a tour-guide robot are presented in order to highlight the relevance and complementarity of the developed visual functions. Extensions are finally discussed.

Abstract

This paper presents a new algorithm for human motion three-dimensional tracking based on a stereo camera system embedded on a mobile robot. The approach mixes advantages of the well-known ICONDENSATION and Annealed particle filters into a more reliable "I-Annealed" particle filter based tracker. Data fusion is also studied to show that a wide variety of visual cues must be used so that the system can adapt to various backgrounds. A complete implementation of the proposed tracker is described as well as some results on indoor sequences. Finally, evolutions and future work are discussed.