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Adaptive Noise Reduction and Voice Activity Detection for improved Verbal Human-Robot Interaction using Binaural Data

Adaptive Noise Reduction and Voice Activity Detection for im...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Robert Brueckmann Andrea Scheidig Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Technical University Ilmenau Germany

Speech has become an important part in human robot interaction (HRI), e.g. for person detection systems by using localized sound sources or for applications in automatic speech recognition (ASR) systems. By using speech in HRI in real world environments, we have to deal with mostly high and varying background noise, reverberation and also with different sound sources superimposing speech and other noises. Therefore, for real world scenarios a suitable signal preprocessing is essential. In this paper, we present a part of the artificial auditory system implemented on the mobile interaction robot HOROS using only two low cost microphones. We combined neural voice activity detection (VAD) and adaptive noise reduction which are essential aspects for HRI using mobile robot systems in changing and populated real-world environments. In the result, our system is able to robustly react on speech signals from its human interaction partner while ignoring other sound sources. Experiments show a significantly improved ASR performance in demanding environments making the system suitable for the use in real-world scenarios.

关键词： Noise reduction Automatic speech recognition Speech enhancement Background noise Mobile robots Human robot interaction Reverberation Acoustic noise Working environment noise Auditory system

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A sensor-independent approach to RBPF SLAM - Map Match SLAM applied to visual mapping

A sensor-independent approach to RBPF SLAM - Map Match SLAM ...

引用

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Christof Schroeter Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

ISBN: (纸本)9781424420575

In this paper, we present the application of our generic, sensor-independent Map Match SLAM framework to visual mapping. In our previous work , we have introduced the map match SLAM approach for mapping with sonar range readings: Extending the grid-based Rao-Blackwellized particle filter SLAM approach, in Map Match SLAM, a local map is maintained by each particle in addition to the global map. The local map is used to represent the most recent observations, and weighting of the particles is done based on the compliance of the local and the global map. In this paper, we show how RBPF SLAM can also be applied for mapping and path reconstruction with a stereo camera or a single monocular camera, respectively. By mapping with completely different sensors such as sonar, stereo, or monocular cameras, we prove the wide range applicability of RBPF SLAM and our map match SLAM computational framework.

关键词： Robot sensing systems Robots Robot kinematics Cameras Robot vision systems Three dimensional displays Sonar

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Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories

Automatic Calibration of Multiple Stationary Laser Range Fin...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Konrad Schenk Alexander Kolarow Markus Eisenbach Klaus Debes Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a global coordinate system is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a global coordinate system, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

关键词： Calibration Robot sensing systems Lasers Legged locomotion Iterative closest point algorithm Measurement by laser beam

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View Invariant Appearance-Based Person Reidentification Using Fast Online Feature Selection and Score Level Fusion

View Invariant Appearance-Based Person Reidentification Usin...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Markus Eisenbach Alexander Kolarow Konrad Schenk Klaus Debes Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

Fast and robust person reidentification is an important task in multi-camera surveillance and automated access control. We present an efficient appearance-based algorithm, able to reidentify a person regardless of occlusions, distance to the camera, and changes in view and lighting. The use of fast online feature selection techniques enables us to perform reidentification in hyper-real-time for a multi-camera system, by taking only 10 seconds for evaluating 100 minutes of HD-video data. We demonstrate, that our approach surpasses current appearance-based state-of-the-art in reidentification quality and computational speed and sets a new reference in non-biometric reidentification.

关键词： Feature extraction Training Surveillance Cameras Computational modeling Real-time systems Robustness

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Playing hide and seek with a mobile companion robot

Playing hide and seek with a mobile companion robot

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IEEE-RAS International Conference on Humanoid Robots

作者： Michael Volkhardt Steffen Mueller Christof Schroeter Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

This paper addresses the problem of finding people in domestic environments utilizing a mobile robot. Companion robots, which should provide different services, must be able to robustly estimate the user's position. While detecting people in an upright pose is mainly solved, most of the users' various poses in living environments are hard to detect. We present a tracking framework that incorporates state-of-the-art detection modules, but also a novel approach for visually detecting the presence of people resting at previously known seating places in arbitrary poses. The method is based on a contextual color model of the respective place in the environment and a color model of the user's appearance. The system has been tested by evaluating the robot's capability to find the user in a 3-room apartment in a hide and seek scenario.

关键词： Histograms Image color analysis Robot sensing systems Context modeling Context

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Whom to talk to? Estimating user interest from movement trajectories

Whom to talk to? Estimating user interest from movement traj...

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17th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2008), vol.2

作者： Steffen Muller Sven Hellbach Erik Schaffernicht Antje Ober Andrea Scheidig Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

Correctly identifying people who are interested in an interaction with a mobile robot is an essential task for a smart Human-Robot Interaction. In this paper an approach is presented for selecting suitable trajectory features in a task specific manner from a huge amount of different forms of possible representations. Different sub-sampling techniques are proposed to generate trajectory sequences from which features are extracted. The trajectory data was generated in real world experiments that include extensive user interviews to acquire information about user behaviors and intentions. Using those feature vectors in a classification method enables the robot to estimate the user's interaction interest. For generating low-dimensional feature vectors, a common method, the Principle Component Analysis, is applied. The selection and combination of useful features out of a set of possible features is carried out by an information theoretic approach based on the Mutual Information and Joint Mutual Information with respect to the user's interaction interest. The introduced procedure is evaluated with neural classifiers, which are trained with the extracted features of the trajectories and the user behavior gained by observation as well as user interviewing. The results achieved indicate that an estimation of the user's interaction interest using trajectory information is feasible.

关键词： Robots

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A graph matching technique for an appearance-based, visual SLAM-approach using Rao-Blackwellized Particle Filters

A graph matching technique for an appearance-based, visual S...

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2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Alexander Koenig Jens Kessler Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

ISBN: (纸本)9781424420575

In continuation of our previous work on visual, appearance-based localization in manually built maps in this paper we present a novel appearance-based, visual SLAM approach. The essential contribution of this work is, an adaptive sensor model which is estimated online and a graph matching scheme to evaluate the likelihood of a given topological map. Both methods enable the combination of an appearance-based, visual localization concept with a Rao-Blackwellized Particle Filter (RBPF) as state estimator to a real-world suitable, online SLAM approach. In our system, each RBPF particle incrementally constructs its own graph-based environment model which is labeled with visual appearance features (extracted from panoramic 360deg snapshots of the environment) and the estimated poses of the places where the snapshots were captured. The essential advantages of this appearance-based SLAM approach are its low memory and computing-time requirements. Therefore, the algorithm is able to perform in real-time. Finally, we present the results of SLAM experiments in two challenging environments that investigate the stability and localization accuracy of this SLAM technique.

关键词： Robots Visualization Robot sensing systems Computational modeling Adaptation model Feature extraction Trajectory

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Automatic calibration of a stationary network of laser range finders by matching movement trajectories

Automatic calibration of a stationary network of laser range...

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2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Konrad Schenk Alexander Kolarow Markus Eisenbach Klaus Debes Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

Laser based detection and tracking of persons can be used for numerous tasks. While a single laser range finder (LRF) is sufficient for detecting and tracking persons on a mobile robot platform, a network of multiple LRF is required to observe persons in larger spaces. Calibrating multiple LRF into a global coordinate system is usually done by hand in a time consuming procedure. An automatic calibration mechanism for such a sensor network is introduced in this paper. Without the need of prior knowledge about the environment, this mechanism is able to obtain the positions and orientations of all LRF in a global coordinate system. By comparing person tracks, determined for each individual LRF unit and matching them, constrains between the LRF units can be calculated. We are able to estimate the poses of all LRF by resolving these constrains. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.

关键词： Calibration Lasers Robot kinematics Robot sensing systems Legged locomotion Synchronization

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Prediction of human collision avoidance behavior by lifelong learning for socially compliant robot navigation

Prediction of human collision avoidance behavior by lifelong...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Christoph Weinrich Michael Volkhardt Erik Einhorn Horst-Michael Gross Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany

In order to act socially compliant with humans, mobile robots need to show several behaviors that require the prediction of people's motion. For example, when a robot avoids a person, it needs to respect the human's personal space [1] and the avoidance behavior needs to be smooth, so that it is understandable to the interaction partner. To achieve this, the robot needs to reason about future paths a person is likely to follow. Because humans adapt their avoidance behavior to the robot's motion, the proposed method performs lifelong learning of the people's behavior while it adapts its own behavior to their motion. The human avoidance behavior is modeled by a discrete, multi-modal, spatio-temporal distribution over the people's future occurrences. This prediction is based on the people's positions and their velocities relatively to the robot and the obstacle situation of the robot's environment. The proposed prediction method is significantly better than a simple linear prediction. Particularly, for tactical decisions, like whether to avoid a moving person on the left or on the right side, this approach is well suited. Furthermore, when the humans get used to a robot, also a long-term change of the human behavior towards the robot can be learned by our approach.

关键词： Robots Cost function Navigation Tracking Collision avoidance Correlation Trajectory

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ShopBot: Progress in developing an interactive mobile shopping assistant for everyday use

ShopBot: Progress in developing an interactive mobile shoppi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： H.-M. Gross H.-J. Boehme C. Schroeter S. Mueller A. Koenig Ch. Martin M. Merten A. Bley Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany MetraLaboratories Robotics GmbH Ilmenau Germany

The paper describes progress achieved in our long-term research project ShopBot, which aims at the development of an intelligent and interactive mobile shopping assistant for everyday use in shopping centers or home improvement stores. It is focusing on recent progress concerning two important methodological aspects: (i) the on-line building of maps of the operation area by means of advanced Rao-Blackwellized SLAM approaches using both sonar-based gridmaps as well as vision-based graph maps as representations, and (ii) a probabilistic approach to multi-modal user detection and tracking during the guidance tour. Experimental results of both the map building characteristics and the person tracking behavior achieved in an ordinary home improvement store demonstrate the reliability of both approaches. Moreover, we present first very encouraging results of long-term field trials which have been executed with three robotic shopping assistants in another home improvement store in Bavaria since March 2008. In this field test, the robots could demonstrate their suitability for this challenging real-world application, as well as the necessary user acceptance.

关键词： Robot sensing systems Sensor phenomena and characterization Mobile robots Simultaneous localization and mapping Sonar detection cognitive robotics Intelligent robots Testing Robot vision systems Navigation

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