2 research outputs found
VIR-SLAM: Visual, Inertial, and Ranging SLAM for single and multi-robot systems
Monocular cameras coupled with inertial measurements generally give high
performance visual inertial odometry. However, drift can be significant with
long trajectories, especially when the environment is visually challenging. In
this paper, we propose a system that leverages ultra-wideband ranging with one
static anchor placed in the environment to correct the accumulated error
whenever the anchor is visible. We also use this setup for collaborative SLAM:
different robots use mutual ranging (when available) and the common anchor to
estimate the transformation between each other, facilitating map fusion Our
system consists of two modules: a double layer ranging, visual, and inertial
odometry for single robots, and a transformation estimation module for
collaborative SLAM. We test our system on public datasets by simulating an
ultra-wideband sensor as well as on real robots. Experiments show our method
can outperform state-of-the-art visual-inertial odometry by more than 20%. For
visually challenging environments, our method works even the visual-inertial
odometry has significant drift Furthermore, we can compute the collaborative
SLAM transformation matrix at almost no extra computation cost
Unique 4-DOF Relative Pose Estimation with Six Distances for UWB/V-SLAM-Based Devices
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