115 research outputs found
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
On-manifold Decentralized State Estimation using Pseudomeasurements and Preintegration
This paper addresses the problem of decentralized, collaborative state
estimation in robotic teams. In particular, this paper considers problems where
individual robots estimate similar physical quantities, such as each other's
position relative to themselves. The use of \emph{pseudomeasurements} is
introduced as a means of modelling such relationships between robots' state
estimates, and is shown to be a tractable way to approach the decentralized
state estimation problem. Moreover, this formulation easily leads to a
general-purpose observability test that simultaneously accounts for
measurements that robots collect from their own sensors, as well as the
communication structure within the team. Finally, input preintegration is
proposed as a communication-efficient way of sharing odometry information
between robots, and the entire theory is appropriate for both vector-space and
Lie-group state definitions. The proposed framework is evaluated on three
different simulated problems, and one experiment involving three quadcopters.Comment: 15 pages, 13 figures, submitted to IEE
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
A parallel hypothesis method of autonomous underwater vehicle navigation
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2009This research presents a parallel hypothesis method for autonomous underwater vehicle
navigation that enables a vehicle to expand the operating envelope of existing
long baseline acoustic navigation systems by incorporating information that is not
normally used. The parallel hypothesis method allows the in-situ identification of
acoustic multipath time-of-flight measurements between a vehicle and an external
transponder and uses them in real-time to augment the navigation algorithm during
periods when direct-path time-of-flight measurements are not available. A proof of
concept was conducted using real-world data obtained by the Woods Hole Oceanographic
Institution Deep Submergence Lab's Autonomous Benthic Explorer (ABE)
and Sentry autonomous underwater vehicles during operations on the Juan de Fuca
Ridge.
This algorithm uses a nested architecture to break the navigation solution down
into basic building blocks for each type of available external information. The algorithm
classifies external information as either line of position or gridded observations.
For any line of position observation, the algorithm generates a multi-modal block
of parallel position estimate hypotheses. The multimodal hypotheses are input into
an arbiter which produces a single unimodal output. If a priori maps of gridded
information are available, they are used within the arbiter structure to aid in the
elimination of false hypotheses. For the proof of concept, this research uses ranges
from a single external acoustic transponder in the hypothesis generation process and
grids of low-resolution bathymetric data from a ship-based multibeam sonar in the
arbitration process.
The major contributions of this research include the in-situ identification of acoustic
multipath time-of-flight measurements, the multiscale utilization of a priori low-resolution
bathymetric data in a high-resolution navigation algorithm, and the design
of a navigation algorithm with a
exible architecture. This flexible architecture allows
the incorporation of multimodal beliefs without requiring a complex mechanism for
real-time hypothesis generation and culling, and it allows the real-time incorporation
of multiple types of external information as they become available in situ into the
overall navigation solution
AIDS drugs for Africa!' a case study examining transnational AIDS treatment activism and the reduction of global antiretroviral prices from 1996 to 2001
Includes abstract.
Includes bibliographical references
An Information Fusion Perspective
A fundamental issue concerned the effectiveness of the Bayesian filter is raised.The observation-only (O2) inference is presented for dynamic state estimation.The "probability of filter benefit" is defined and quantitatively analyzed.Convincing simulations demonstrate that many filters can be easily ineffective. The general solution for dynamic state estimation is to model the system as a hidden Markov process and then employ a recursive estimator of the prediction-correction format (of which the best known is the Bayesian filter) to statistically fuse the time-series observations via models. The performance of the estimator greatly depends on the quality of the statistical mode assumed. In contrast, this paper presents a modeling-free solution, referred to as the observation-only (O2) inference, which infers the state directly from the observations. A Monte Carlo sampling approach is correspondingly proposed for unbiased nonlinear O2 inference. With faster computational speed, the performance of the O2 inference has identified a benchmark to assess the effectiveness of conventional recursive estimators where an estimator is defined as effective only when it outperforms on average the O2 inference (if applicable). It has been quantitatively demonstrated, from the perspective of information fusion, that a prior "biased" information (which inevitably accompanies inaccurate modelling) can be counterproductive for a filter, resulting in an ineffective estimator. Classic state space models have shown that a variety of Kalman filters and particle filters can easily be ineffective (inferior to the O2 inference) in certain situations, although this has been omitted somewhat in the literature
A Decentralized Architecture for Active Sensor Networks
This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms
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