1,750 research outputs found
Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds
In this paper we address the problems of modeling the acoustic space
generated by a full-spectrum sound source and of using the learned model for
the localization and separation of multiple sources that simultaneously emit
sparse-spectrum sounds. We lay theoretical and methodological grounds in order
to introduce the binaural manifold paradigm. We perform an in-depth study of
the latent low-dimensional structure of the high-dimensional interaural
spectral data, based on a corpus recorded with a human-like audiomotor robot
head. A non-linear dimensionality reduction technique is used to show that
these data lie on a two-dimensional (2D) smooth manifold parameterized by the
motor states of the listener, or equivalently, the sound source directions. We
propose a probabilistic piecewise affine mapping model (PPAM) specifically
designed to deal with high-dimensional data exhibiting an intrinsic piecewise
linear structure. We derive a closed-form expectation-maximization (EM)
procedure for estimating the model parameters, followed by Bayes inversion for
obtaining the full posterior density function of a sound source direction. We
extend this solution to deal with missing data and redundancy in real world
spectrograms, and hence for 2D localization of natural sound sources such as
speech. We further generalize the model to the challenging case of multiple
sound sources and we propose a variational EM framework. The associated
algorithm, referred to as variational EM for source separation and localization
(VESSL) yields a Bayesian estimation of the 2D locations and time-frequency
masks of all the sources. Comparisons of the proposed approach with several
existing methods reveal that the combination of acoustic-space learning with
Bayesian inference enables our method to outperform state-of-the-art methods.Comment: 19 pages, 9 figures, 3 table
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Sensorimotor embedding : a developmental approach to learning geometry
textA human infant facing the blooming, buzzing confusion of the senses grows up to be an adult with common-sense knowledge of geometry; this knowledge then allows her to describe the shapes of objects, the layouts of places, and the relative locations of things naturally and effortlessly. In robotics, such knowledge is usually built in by a human designer who needs to solve complex engineering problems of sensor calibration and inference. In contrast, this dissertation presents a model for how autonomous agents can form an understanding of geometry the same way infants do: by learning from early unstructured sensorimotor experience.
Through a framework called sensorimotor embedding, an agent reconstructs knowledge of its own sensor structure, the local geometry of the world, and the pose of objects within the world. The validity of this knowledge is demonstrated directly through Procrustes analysis and indirectly by using it to solve the mountain car task with different morphologies. The dissertation demonstrates how sensorimotor embedding can serve as a robust approach for acquiring geometric knowledge.Computer Science
Manifold-Based Sensorimotor Representations for Bootstrapping of Mobile Agents
Subject of this thesis is the development of a domain-independent algorithm that allows an autonomous system to process sequences of the sensorimotor interaction with its environment and to assign a geometric interpretation to its motor capabilities. We utilize Lie groups, smooth manifolds endowed with a group structure, that allow for an elegant representation of geometric operations as a central foundation for such a sensorimotor representation. Expressing motor controls with respect to the manifold structure allows us to transform the sensorimotor interaction sequence into a specific set of data points. Finding a manifold and a transformation that minimizes an intrinsic conflict function corresponds to finding a topological structure that is the best fit for expressing the sensorimotor space the entity resides in. Experiments in a virtual environment are conducted that show the applicability of the approach with respect to different sensor and motor configurations
A Spectral Learning Approach to Range-Only SLAM
We present a novel spectral learning algorithm for simultaneous localization
and mapping (SLAM) from range data with known correspondences. This algorithm
is an instance of a general spectral system identification framework, from
which it inherits several desirable properties, including statistical
consistency and no local optima. Compared with popular batch optimization or
multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral
approach offers guaranteed low computational requirements and good tracking
performance. Compared with popular extended Kalman filter (EKF) or extended
information filter (EIF) approaches, and many MHT ones, our approach does not
need to linearize a transition or measurement model; such linearizations can
cause severe errors in EKFs and EIFs, and to a lesser extent MHT, particularly
for the highly non-Gaussian posteriors encountered in range-only SLAM. We
provide a theoretical analysis of our method, including finite-sample error
bounds. Finally, we demonstrate on a real-world robotic SLAM problem that our
algorithm is not only theoretically justified, but works well in practice: in a
comparison of multiple methods, the lowest errors come from a combination of
our algorithm with batch optimization, but our method alone produces nearly as
good a result at far lower computational cost
Visual SLAM using straight lines
The present thesis is focuses on the problem of Simultaneous Localisation and
Mapping (SLAM) using only visual data (VSLAM). This means to concurrently estimate the position of a moving camera and to create a consistent map of the environment.
Since implementing a whole VSLAM system is out of the scope of a degree thesis, the main aim is to improve an existing visual SLAM system by complementing the commonly used point features with straight line primitives. This enables more accurate localization in environments with few feature points, like corridors.
As a foundation for the project, ScaViSLAM by Strasdat et al. is used, which is a state-of-the-art real-time visual SLAM framework. Since it currently only supports Stereo and RGB-D systems, implementing a Monocular approach will be researched as well as an integration of it as a ROS package in order to deploy it on a mobile robot.
For the experimental results, the Care-O-bot service robot developed by Fraunhofer IPA will be used
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