2,000 research outputs found
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
Pose Estimation for Omni-directional Cameras using Sinusoid Fitting
We propose a novel pose estimation method for geometric vision of
omni-directional cameras. On the basis of the regularity of the pixel movement
after camera pose changes, we formulate and prove the sinusoidal relationship
between pixels movement and camera motion. We use the improved Fourier-Mellin
invariant (iFMI) algorithm to find the motion of pixels, which was shown to be
more accurate and robust than the feature-based methods. While iFMI works only
on pin-hole model images and estimates 4 parameters (x, y, yaw, scaling), our
method works on panoramic images and estimates the full 6 DoF 3D transform, up
to an unknown scale factor. For that we fit the motion of the pixels in the
panoramic images, as determined by iFMI, to two sinusoidal functions. The
offsets, amplitudes and phase-shifts of the two functions then represent the 3D
rotation and translation of the camera between the two images. We perform
experiments for 3D rotation, which show that our algorithm outperforms the
feature-based methods in accuracy and robustness. We leave the more complex 3D
translation experiments for future work.Comment: 8 pages, 5 figures, 1 tabl
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
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