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A Multiple UAV System for Vision-Based Search and Localization
University of California, Berkeley, CA, USA.
University of California, Berkeley, CA, USA.
University of California, Berkeley, CA, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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2008 (English)In: Proceedings of the '08 American Control Conference, IEEE , 2008, 1985-1990 p.Conference paper (Refereed)
Abstract [en]

The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined.

Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error covariance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

Place, publisher, year, edition, pages
IEEE , 2008. 1985-1990 p.
Keyword [en]
Bayes methods, Aerospace control, Aircraft, Cameras, Particle filtering
National Category
Engineering and Technology Control Engineering
URN: urn:nbn:se:liu:diva-15504DOI: 10.1109/ACC.2008.4586784ISBN: 978-1-4244-2079-7ISBN: 978-1-4244-2078-0OAI: diva2:117424
'08 American Control Conference, Seattle, WA, USA, June, 2008
Available from: 2008-11-12 Created: 2008-11-12 Last updated: 2013-12-03Bibliographically approved
In thesis
1. Estimation and Detection with Applications to Navigation
Open this publication in new window or tab >>Estimation and Detection with Applications to Navigation
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ability to navigate in an unknown environment is an enabler for truly utonomous systems. Such a system must be aware of its relative position to the surroundings using sensor measurements. It is instrumental that these measurements are monitored for disturbances and faults. Having correct measurements, the challenging problem for a robot is to estimate its own position and simultaneously build a map of the environment. This problem is referred to as the Simultaneous Localization and Mapping (SLAM) problem. This thesis studies several topics related to SLAM, on-board sensor processing, exploration and disturbance detection.

The particle filter (PF) solution to the SLAM problem is commonly referred to as FastSLAM and has been used extensively for ground robot applications. Having more complex vehicle models using for example flying robots extends the state dimension of the vehicle model and makes the existing solution computationally infeasible. The factorization of the problem made in this thesis allows for a computationally tractable solution.

Disturbance detection for magnetometers and detection of spurious features in image sensors must be done before these sensor measurements can be used for estimation. Disturbance detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. There are two approaches to this problem. One is based on the traditional parity space method, where the influence of the initial state is removed by projection, and the other on combining prior information with data in the batch. An efficient parameterization of incipient faults is given which is shown to improve the results considerably.

Another common situation in robotics is to have different sampling rates of the sensors. More complex sensors such as cameras often have slower update rate than accelerometers and gyroscopes. An algorithm for this situation is derived for a class of models with linear Gaussian dynamic model and sensors with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the Kalman filter is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

Vision based target tracking is another important estimation problem in robotics. Distributed exploration with multi-aircraft flight experiments has demonstrated localization of a stationary target with estimate covariance on the order of meters. Grid-based estimation as well as the PF have been examined.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. 174 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1216
Navigation, SLAM, Particle Filter, Estimation
National Category
Engineering and Technology
urn:nbn:se:liu:diva-14956 (URN)978-91-7393-785-6 (ISBN)
Public defence
2008-11-05, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
The third article in this thesis is included with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Linköping University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this material, you agree to all provisions of the copyright laws protecting it. Please be advised that wherever a copyright notice from another organization is displayed beneath a figure, a photo, a videotape or a Powerpoint presentation, you must get permission from that organization, as IEEE would not be the copyright holder.Available from: 2008-11-12 Created: 2008-10-03 Last updated: 2009-05-18Bibliographically approved

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