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

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 20; p. 4366
• Main Authors: Molina Martel, Francisco, Sidorenko, Juri, Bodensteiner, Christoph, Arens, Michael, Hugentobler, Urs
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09.10.2019; MDPI
• Subjects: Algorithms; augmented reality (ar); c-slam; Cameras; Localization; Methods; Noise; relative localization; relative pose estimation; Robots; sensor fusion; ultra-wideband (uwb); Unmanned aerial vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19204366

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.