Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency

Acoustic SLAM based on the Direction-of-Arrival and the Direct-to-Reverberant Energy Ratio

This paper proposes a new method that fuses acoustic measurements in the reverberation field and low-accuracy inertial measurement unit (IMU) motion reports for simultaneous localization and mapping (SLAM). Different from existing studies that only use acoustic data for direction-of-arrival (DoA) estimates, the source's distance from sensors is calculated with the direct-to-reverberant energy ratio (DRR) and applied as a new constraint to eliminate the nonlinear noise from motion reports. A particle filter is applied to estimate the critical distance, which is key for associating the source's distance with the DRR. A keyframe method is used to eliminate the deviation of the source position estimation toward the robot. The proposed DoA-DRR acoustic SLAM (D-D SLAM) is designed for three-dimensional motion and is suitable for most robots. The method is the first acoustic SLAM algorithm that has been validated on a real-world indoor scene dataset that contains only acoustic data and IMU measurements. Compared with previous methods, D-D SLAM has acceptable performance in locating the robot and building a source map from a real-world indoor dataset. The average location accuracy is 0.48 m, while the source position error converges to less than 0.25 m within 2.8 s. These results prove the effectiveness of D-D SLAM in real-world indoor scenes, which may be especially useful in search and rescue missions after disasters where the environment is foggy, i.e., unsuitable for light or laser irradiation

Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

Secure indoor navigation and operation of mobile robots

In future work environments, robots will navigate and work side by side to humans. This raises big challenges related to the safety of these robots. In this Dissertation, three tasks have been realized: 1) implementing a localization and navigation system based on StarGazer sensor and Kalman filter; 2) realizing a human-robot interaction system using Kinect sensor and BPNN and SVM models to define the gestures and 3) a new collision avoidance system is realized. The system works on generating the collision-free paths based on the interaction between the human and the robot.In zukünftigen Arbeitsumgebungen werden Roboter navigieren nebeneinander an Menschen. Das wirft Herausforderungen im Zusammenhang mit der Sicherheit dieser Roboter auf. In dieser Dissertation drei Aufgaben realisiert: 1. Implementierung eines Lokalisierungs und Navigationssystem basierend auf Kalman Filter: 2. Realisierung eines Mensch-Roboter-Interaktionssystem mit Kinect und AI zur Definition der Gesten und 3. ein neues Kollisionsvermeidungssystem wird realisiert. Das System arbeitet an der Erzeugung der kollisionsfreien Pfade, die auf der Wechselwirkung zwischen dem Menschen und dem Roboter basieren

Sonar attentive underwater navigation in structured environment

One of the fundamental requirements of a persistently Autonomous Underwater Vehicle (AUV) is a robust navigation system. The success of most complex robotic tasks depends on the accuracy of a vehicle’s navigation system. In a basic form, an AUV estimates its position using an on-board navigation sensors through Dead-Reckoning (DR). However DR navigation systems tends to drift in the long run due to accumulated measurement errors. One way of mitigating this problem require the use of Simultaneous Localization and Mapping (SLAM) by concurrently mapping external environment features. The performance of a SLAM navigation system depends on the availability of enough good features in the environment. On the contrary, a typical underwater structured environment (harbour, pier or oilfield) has a limited amount of sonar features in a limited locations, hence exploitation of good features is a key for effective underwater SLAM. This thesis develops a novel attentive sonar line feature based SLAM framework that improves the performance of a SLAM navigation by steering a multibeam sonar sensor,which is mounted on a pan and tilt unit, towards feature-rich regions of the environment. A sonar salience map is generated at each vehicle pose to identify highly informative and stable regions of the environment. Results from a simulated test and real AUV experiment show an attentive SLAM performs better than a passive counterpart by repeatedly visiting good sonar landmarks