4,856 research outputs found
Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images
Fast and robust image matching is a very important task with various
applications in computer vision and robotics. In this paper, we compare the
performance of three different image matching techniques, i.e., SIFT, SURF, and
ORB, against different kinds of transformations and deformations such as
scaling, rotation, noise, fish eye distortion, and shearing. For this purpose,
we manually apply different types of transformations on original images and
compute the matching evaluation parameters such as the number of key points in
images, the matching rate, and the execution time required for each algorithm
and we will show that which algorithm is the best more robust against each kind
of distortion. Index Terms-Image matching, scale invariant feature transform
(SIFT), speed up robust feature (SURF), robust independent elementary features
(BRIEF), oriented FAST, rotated BRIEF (ORB).Comment: 5 pages, 6 figures, In Proceedings of the 2015 Newfoundland
Electrical and Computer Engineering Conference,St. johns, Canada, November,
201
A Hybrid SLAM and Object Recognition System for Pepper Robot
Humanoid robots are playing increasingly important roles in real-life tasks
especially when it comes to indoor applications. Providing robust solutions for
the tasks such as indoor environment mapping, self-localisation and object
recognition are essential to make the robots to be more autonomous, hence, more
human-like. The well-known Aldebaran service robot Pepper is a suitable
candidate for achieving these goals. In this paper, a hybrid system combining
Simultaneous Localisation and Mapping (SLAM) algorithm with object recognition
is developed and tested with Pepper robot in real-world conditions for the
first time. The ORB SLAM 2 algorithm was taken as a seminal work in our
research. Then, an object recognition technique based on Scale-Invariant
Feature Transform (SIFT) and Random Sample Consensus (RANSAC) was combined with
SLAM to recognise and localise objects in the mapped indoor environment. The
results of our experiments showed the system's applicability for the Pepper
robot in real-world scenarios. Moreover, we made our source code available for
the community at https://github.com/PaolaArdon/Salt-Pepper.Comment: All authors contributed equally, listed in alphabetical orde
CFORB: Circular FREAK-ORB Visual Odometry
We present a novel Visual Odometry algorithm entitled Circular FREAK-ORB
(CFORB). This algorithm detects features using the well-known ORB algorithm
[12] and computes feature descriptors using the FREAK algorithm [14]. CFORB is
invariant to both rotation and scale changes, and is suitable for use in
environments with uneven terrain. Two visual geometric constraints have been
utilized in order to remove invalid feature descriptor matches. These
constraints have not previously been utilized in a Visual Odometry algorithm. A
variation to circular matching [16] has also been implemented. This allows
features to be matched between images without having to be dependent upon the
epipolar constraint. This algorithm has been run on the KITTI benchmark dataset
and achieves a competitive average translational error of and average
rotational error of . CFORB has also been run in an indoor
environment and achieved an average translational error of . After
running CFORB in a highly textured environment with an approximately uniform
feature spread across the images, the algorithm achieves an average
translational error of and an average rotational error of
MODS: Fast and Robust Method for Two-View Matching
A novel algorithm for wide-baseline matching called MODS - Matching On Demand
with view Synthesis - is presented. The MODS algorithm is experimentally shown
to solve a broader range of wide-baseline problems than the state of the art
while being nearly as fast as standard matchers on simple problems. The
apparent robustness vs. speed trade-off is finessed by the use of progressively
more time-consuming feature detectors and by on-demand generation of
synthesized images that is performed until a reliable estimate of geometry is
obtained.
We introduce an improved method for tentative correspondence selection,
applicable both with and without view synthesis. A modification of the standard
first to second nearest distance rule increases the number of correct matches
by 5-20% at no additional computational cost.
Performance of the MODS algorithm is evaluated on several standard publicly
available datasets, and on a new set of geometrically challenging wide baseline
problems that is made public together with the ground truth. Experiments show
that the MODS outperforms the state-of-the-art in robustness and speed.
Moreover, MODS performs well on other classes of difficult two-view problems
like matching of images from different modalities, with wide temporal baseline
or with significant lighting changes.Comment: Version accepted to CVIU. arXiv admin note: text overlap with
arXiv:1306.385
Local Multi-Grouped Binary Descriptor with Ring-based Pooling Configuration and Optimization
Local binary descriptors are attracting increasingly attention due to their
great advantages in computational speed, which are able to achieve real-time
performance in numerous image/vision applications. Various methods have been
proposed to learn data-dependent binary descriptors. However, most existing
binary descriptors aim overly at computational simplicity at the expense of
significant information loss which causes ambiguity in similarity measure using
Hamming distance. In this paper, by considering multiple features might share
complementary information, we present a novel local binary descriptor, referred
as Ring-based Multi-Grouped Descriptor (RMGD), to successfully bridge the
performance gap between current binary and floated-point descriptors. Our
contributions are two-fold. Firstly, we introduce a new pooling configuration
based on spatial ring-region sampling, allowing for involving binary tests on
the full set of pairwise regions with different shapes, scales and distances.
This leads to a more meaningful description than existing methods which
normally apply a limited set of pooling configurations. Then, an extended
Adaboost is proposed for efficient bit selection by emphasizing high variance
and low correlation, achieving a highly compact representation. Secondly, the
RMGD is computed from multiple image properties where binary strings are
extracted. We cast multi-grouped features integration as rankSVM or sparse SVM
learning problem, so that different features can compensate strongly for each
other, which is the key to discriminativeness and robustness. The performance
of RMGD was evaluated on a number of publicly available benchmarks, where the
RMGD outperforms the state-of-the-art binary descriptors significantly.Comment: To appear in IEEE Trans. on Image Processing, 201
Characterizing SLAM Benchmarks and Methods for the Robust Perception Age
The diversity of SLAM benchmarks affords extensive testing of SLAM algorithms
to understand their performance, individually or in relative terms. The ad-hoc
creation of these benchmarks does not necessarily illuminate the particular
weak points of a SLAM algorithm when performance is evaluated. In this paper,
we propose to use a decision tree to identify challenging benchmark properties
for state-of-the-art SLAM algorithms and important components within the SLAM
pipeline regarding their ability to handle these challenges. Establishing what
factors of a particular sequence lead to track failure or degradation relative
to these characteristics is important if we are to arrive at a strong
understanding for the core computational needs of a robust SLAM algorithm.
Likewise, we argue that it is important to profile the computational
performance of the individual SLAM components for use when benchmarking. In
particular, we advocate the use of time-dilation during ROS bag playback, or
what we refer to as slo-mo playback. Using slo-mo to benchmark SLAM
instantiations can provide clues to how SLAM implementations should be improved
at the computational component level. Three prevalent VO/SLAM algorithms and
two low-latency algorithms of our own are tested on selected typical sequences,
which are generated from benchmark characterization, to further demonstrate the
benefits achieved from computationally efficient components.Comment: 7 pages, 5 figures, accepted at ICRA 2019 Workshop on Dataset
Generation and Benchmarking of SLAM Algorithms for Robotics and VR/A
LoopSmart: Smart Visual SLAM Through Surface Loop Closure
We present a visual simultaneous localization and mapping (SLAM) framework of
closing surface loops. It combines both sparse feature matching and dense
surface alignment. Sparse feature matching is used for visual odometry and
globally camera pose fine-tuning when dense loops are detected, while dense
surface alignment is the way of closing large loops and solving surface
mismatching problem. To achieve smart dense surface loop closure, a highly
efficient CUDA-based global point cloud registration method and a map content
dependent loop verification method are proposed. We run extensive experiments
on different datasets, our method outperforms state-of-the-art ones in terms of
both camera trajectory and surface reconstruction accuracy
Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations
Image identification is one of the most challenging tasks in different areas
of computer vision. Scale-invariant feature transform is an algorithm to detect
and describe local features in images to further use them as an image matching
criteria. In this paper, the performance of the SIFT matching algorithm against
various image distortions such as rotation, scaling, fisheye and motion
distortion are evaluated and false and true positive rates for a large number
of image pairs are calculated and presented. We also evaluate the distribution
of the matched keypoint orientation difference for each image deformation.Comment: 4 pages, 11 figures, In Proceedings of the 2015 Newfoundland
Electrical and Computer Engineering Conference,St. johns, Canada, November,
201
New Feature Detection Mechanism for Extended Kalman Filter Based Monocular SLAM with 1-Point RANSAC
We present a different approach of feature point detection for improving the
accuracy of SLAM using single, monocular camera. Traditionally, Harris Corner
detection, SURF or FAST corner detectors are used for finding feature points of
interest in the image. We replace this with another approach, which involves
building a non-linear scale-space representation of images using Perona and
Malik Diffusion equation and computing the scale normalized Hessian at multiple
scale levels (KAZE feature). The feature points so detected are used to
estimate the state and pose of a mono camera using extended Kalman filter. By
using accelerated KAZE features and a more rigorous feature rejection routine
combined with 1-point RANSAC for outlier rejection, short baseline matching of
features are significantly improved, even with a lesser number of feature
points, especially in the presence of motion blur. We present a comparative
study of our proposal with FAST and show improved localization accuracy in
terms of absolute trajectory error.Comment: Accepted in Third International Conference of Mining Intelligence and
Knowledge Exploration (MIKE) 201
ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion
Structure-from-motion (SfM) largely relies on feature tracking. In image
sequences, if disjointed tracks caused by objects moving in and out of the
field of view, occasional occlusion, or image noise, are not handled well,
corresponding SfM could be affected. This problem becomes severer for
large-scale scenes, which typically requires to capture multiple sequences to
cover the whole scene. In this paper, we propose an efficient non-consecutive
feature tracking (ENFT) framework to match interrupted tracks distributed in
different subsequences or even in different videos. Our framework consists of
steps of solving the feature `dropout' problem when indistinctive structures,
noise or large image distortion exists, and of rapidly recognizing and joining
common features located in different subsequences. In addition, we contribute
an effective segment-based coarse-to-fine SfM algorithm for robustly handling
large datasets. Experimental results on challenging video data demonstrate the
effectiveness of the proposed system.Comment: 15 pages, 12 figure
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