9,174 research outputs found
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This paper presents a novel tightly-coupled keyframe-based Simultaneous
Localization and Mapping (SLAM) system with loop-closing and relocalization
capabilities targeted for the underwater domain. Our previous work, SVIn,
augmented the state-of-the-art visual-inertial state estimation package OKVIS
to accommodate acoustic data from sonar in a non-linear optimization-based
framework. This paper addresses drift and loss of localization -- one of the
main problems affecting other packages in underwater domain -- by providing the
following main contributions: a robust initialization method to refine scale
using depth measurements, a fast preprocessing step to enhance the image
quality, and a real-time loop-closing and relocalization method using bag of
words (BoW). An additional contribution is the addition of depth measurements
from a pressure sensor to the tightly-coupled optimization formulation.
Experimental results on datasets collected with a custom-made underwater sensor
suite and an autonomous underwater vehicle from challenging underwater
environments with poor visibility demonstrate performance never achieved before
in terms of accuracy and robustness
Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-cameras
Spectral methods such as the improved Fourier Mellin Invariant (iFMI)
transform have proved faster, more robust and accurate than feature based
methods on image registration. However, iFMI is restricted to work only when
the camera moves in 2D space and has not been applied on omni-cameras images so
far. In this work, we extend the iFMI method and apply a motion model to
estimate an omni-camera's pose when it moves in 3D space. This is particularly
useful in field robotics applications to get a rapid and comprehensive view of
unstructured environments, and to estimate robustly the robot pose. In the
experiment section, we compared the extended iFMI method against ORB and AKAZE
feature based approaches on three datasets showing different type of
environments: office, lawn and urban scenery (MPI-omni dataset). The results
show that our method boosts the accuracy of the robot pose estimation two to
four times with respect to the feature registration techniques, while offering
lower processing times. Furthermore, the iFMI approach presents the best
performance against motion blur typically present in mobile robotics.Comment: 5 pages, 4 figures, 1 tabl
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