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Unique 4-DOF Relative Pose Estimation with Six Distances for UWB/V-SLAM-Based Devices

 
: Molina Martel, Francisco; Sidorenko, Juri; Bodensteiner, Christoph; Arens, Michael; Hugentobler, Urs

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Volltext ()

Sensors. Online journal 19 (2019), Nr.20, Art. 4366, 14 S.
http://www.mdpi.com/journal/sensors
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IOSB ()
sensor fusion; relative pose estimation; relative localization; ultra-wideband (UWB)

Abstract
In this work we introduce a relative localization method that estimates the coordinate frame transformation between two devices based on distance measurements. We present a linear algorithm that calculates the relative pose in 2D or 3D with four degrees of freedom (4-DOF). This algorithm needs a minimum of five or six distance measurements, respectively, to estimate the relative pose uniquely. We use the linear algorithm in conjunction with outlier detection algorithms and as a good initial estimate for iterative least squares refinement. The proposed method outperforms other related linear methods in terms of distance measurements needed and in terms of accuracy. In comparison with a related linear algorithm in 2D, we can reduce 10% of the translation error. In contrast to the more general 6-DOF linear algorithm, our 4-DOF method reduces the minimum distances needed from ten to six and the rotation error by a factor of four at the standard deviation of our ultra-wideband (UWB) transponders. When using the same amount of measurements the orientation error and translation error are approximately reduced to a factor of ten. We validate our method with simulations and an experimental setup, where we integrate ultra-wideband (UWB) technology into simultaneous localization and mapping (SLAM)-based devices. The presented relative pose estimation method is intended for use in augmented reality applications for cooperative localization with head-mounted displays. We foresee practical use cases of this method in cooperative SLAM, where map merging is performed in the most proactive manner.

: http://publica.fraunhofer.de/dokumente/N-564800.html