Patrick Danès

Broadband variations of the MUSIC high-resolution method for Sound Source Localization in Robotics

The MUSIC algorithm (multiple signal classification) is a well-known high-resolution method to sound source localization. However, as it is essentially narrowband, several extensions can be envisaged when dealing with broadband sources like human voice. This paper presents such extensions and proposes a comparative study w.r.t. specific robotics constraints. An online beamspace MUSIC method, together with a recently developed beamforming scheme, are shown to constitute a mathematically sound and potentially efficient solution.

Rackham: An Interactive Robot-Guide

Rackham is an interactive robot-guide that has been used in several places and exhibitions. This paper presents its design and reports on results that have been obtained after its deployment in a permanent exhibition. The project is conducted so as to incrementally enhance the robot functional and decisional capabilities based on the observation of the interaction between the public and the robot. Besides robustness and efficiency in the robot navigation abilities in a dynamic environment, our focus was to develop and test a methodology to integrate human-robot interaction abilities in a systematic way. We first present the robot and some of its key design issues. Then, we discuss a number of lessons that we have drawn from its use in interaction with the public and how that will serve to refine our design choices and to enhance robot efficiency and acceptability

A survey on sound source localization in robotics: From binaural to array processing methods☆☆☆

This paper attempts to provide a state-of-the-art of sound source localization in Robotics. Noticeably, this context raises original constraints--e.g. embeddability, real time, broad- band environments, noise and reverberation--which are seldom simultaneously taken into account in Acoustics or Signal Processing. A comprehensive review is proposed of recent robotics achievements, be they binaural or rooted in Array Processing techniques. The connections are highlighted with the underlying theory as well as with elements of physiology and neurology of human hearing.

Modal Analysis Based Beamforming for Nearfield or Farfield Speaker Localization in Robotics

This paper describes a broadband beampattern synthesis method for sound source localization in the nearfield or in the farfield of a mobile robot, with a small-size linear array. The method is based on the theory of modal analysis and involves an original convex optimization procedure which benefits from the Parseval relation. The optimized beampattern is obtained by numerically minimizing the worst-case error between the modal coefficients of the array response and those of the reference beampattern, up to a finite rank of the series expansion, over a frequency grid. Simulations illustrate the analytical development

Binaural Systems in Robotics

Audition is often described by physiologists as the most important sense in humans, due to its essential role in communication and socialization. But quite surprisingly, the interest of this modality for robotics arose only in the 2000s, brought to evidence by cognitive robotics and Human–robot interaction. Since then, numerous contributions have been proposed to the field of robot audition, ranging from sound localization to scene analysis. Binaural approaches were investigated first, then became forsaken due to mixed results. Nevertheless, the last years have witnessed a renewal of interest in binaural active audition, that is, in the opportunities and challenges opened by the coupling of binaural sensing and robot motion. This chapter proposes a comprehensive state of the art of binaural approaches to robot audition. Though the literature on binaural audition and, more generally, on acoustics and signal processing, is a fundamental source of knowledge, the tasks, constraints, and environments of robotics raise original issues. These are reviewed, prior to the most prominent contributions, platforms and projects. Two lines of research in binaural active audition, conducted by the current authors, are then outlined, one of which is tightly connected to psychology of perception.

Active binaural localization of intermittent moving sources in the presence of false measurements

This paper takes place within the field of active sound source localization in a binaural context. A stochastic filtering strategy is presented for the localization of a still or moving source from a moving binaural sensor. The proposed method accounts for the source intermittence as well as for false measurements induced by the non-stationarity of the emitted signal. Its effectiveness is showed on experimental results.

Particle filtering strategies for data fusion dedicated to visual tracking from a mobile robot

This paper introduces data fusion strategies within particle filtering in order to track people from a single camera mounted on a mobile robot in a human environment. Various visual cues are described, relying on color, shape or motion, together with several filtering strategies taking into account all or parts of these measurements in their importance and/or measurement functions. A preliminary evaluation enables the selection of the most meaningful visual cues associations in terms of discriminative power, robustness to artifacts and time consumption. The depicted filtering strategies are then evaluated in order to check which people trackers regarding visual cues and algorithms associations best fulfill the requirements of the considered scenarios. The performances are compared through some quantitative and qualitative evaluations. Some associations of filtering strategies and visual cues show a significant increase in the tracking robustness and precision. Future works are finally discussed.

Acoustic models and Kalman filtering strategies for active binaural sound localization

This paper deals with binaural sound localization. An active strategy is proposed, relying on a precise model of the dynamic changes induced by motion on the auditive perception. The proposed framework allows motions of both the sound source and the sensor. The resulting stochastic discrete-time model is then exploited together with Unscented Kalman filtering to provide range and azimuth estimation. Simulations and experiments show the effectiveness of the method.

Extending the IMM filter to Heterogeneous-Order state space models

An extension of the celebrated Interacting Multiple Model (IMM) filter is proposed so as to handle a bank of models with state vectors of heterogeneous size and meaning. The derivation of the IMMHO (IMM Heterogeneous-Order) filter is exposed. It is argued that a component-wise cooperation strategy leads to a sound and versatile solution. Simulation results in the context of satellite telemetry, for which the prior dynamics of the moving target is poorly known, show that the filter is well-behaved, with adequate operation across the various modes. All the theoretical background is provided so that the paper is self-contained.

Human / robot visual interaction for a tour-guide robot

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.