34,840 research outputs found
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
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
We present a robust estimator for fitting multiple parametric models of the
same form to noisy measurements. Applications include finding multiple
vanishing points in man-made scenes, fitting planes to architectural imagery,
or estimating multiple rigid motions within the same sequence. In contrast to
previous works, which resorted to hand-crafted search strategies for multiple
model detection, we learn the search strategy from data. A neural network
conditioned on previously detected models guides a RANSAC estimator to
different subsets of all measurements, thereby finding model instances one
after another. We train our method supervised as well as self-supervised. For
supervised training of the search strategy, we contribute a new dataset for
vanishing point estimation. Leveraging this dataset, the proposed algorithm is
superior with respect to other robust estimators as well as to designated
vanishing point estimation algorithms. For self-supervised learning of the
search, we evaluate the proposed algorithm on multi-homography estimation and
demonstrate an accuracy that is superior to state-of-the-art methods.Comment: CVPR 202
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