1,793 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
The Conditional Lucas & Kanade Algorithm
The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense
image and object alignment. The approach is efficient as it attempts to model
the connection between appearance and geometric displacement through a linear
relationship that assumes independence across pixel coordinates. A drawback of
the approach, however, is its generative nature. Specifically, its performance
is tightly coupled with how well the linear model can synthesize appearance
from geometric displacement, even though the alignment task itself is
associated with the inverse problem. In this paper, we present a new approach,
referred to as the Conditional LK algorithm, which: (i) directly learns linear
models that predict geometric displacement as a function of appearance, and
(ii) employs a novel strategy for ensuring that the generative pixel
independence assumption can still be taken advantage of. We demonstrate that
our approach exhibits superior performance to classical generative forms of the
LK algorithm. Furthermore, we demonstrate its comparable performance to
state-of-the-art methods such as the Supervised Descent Method with
substantially less training examples, as well as the unique ability to "swap"
geometric warp functions without having to retrain from scratch. Finally, from
a theoretical perspective, our approach hints at possible redundancies that
exist in current state-of-the-art methods for alignment that could be leveraged
in vision systems of the future.Comment: 17 pages, 11 figure
Efficient Model-Based 3D Tracking of Deformable Objects
Efficient incremental image alignment is a topic of renewed interest in the computer vision community because of its applications in model fitting and model-based object tracking. Successful compositional procedures for aligning 2D and 3D models under weak-perspective imaging conditions have already been proposed. Here we present a mixed compositional and additive algorithm which is applicable to the full projective camera case
Efficient illumination independent appearance-based face tracking
One of the major challenges that visual tracking algorithms face nowadays is being
able to cope with changes in the appearance of the target during tracking. Linear
subspace models have been extensively studied and are possibly the most popular
way of modelling target appearance. We introduce a linear subspace representation
in which the appearance of a face is represented by the addition of two approxi-
mately independent linear subspaces modelling facial expressions and illumination
respectively. This model is more compact than previous bilinear or multilinear ap-
proaches. The independence assumption notably simplifies system training. We only
require two image sequences. One facial expression is subject to all possible illumina-
tions in one sequence and the face adopts all facial expressions under one particular
illumination in the other. This simple model enables us to train the system with
no manual intervention. We also revisit the problem of efficiently fitting a linear
subspace-based model to a target image and introduce an additive procedure for
solving this problem. We prove that Matthews and Baker’s Inverse Compositional
Approach makes a smoothness assumption on the subspace basis that is equiva-
lent to Hager and Belhumeur’s, which worsens convergence. Our approach differs
from Hager and Belhumeur’s additive and Matthews and Baker’s compositional ap-
proaches in that we make no smoothness assumptions on the subspace basis. In the
experiments conducted we show that the model introduced accurately represents
the appearance variations caused by illumination changes and facial expressions.
We also verify experimentally that our fitting procedure is more accurate and has
better convergence rate than the other related approaches, albeit at the expense of
a slight increase in computational cost. Our approach can be used for tracking a
human face at standard video frame rates on an average personal computer
The Study and Literature Review of a Feature Extraction Mechanism in Computer Vison
Detecting the Features in the image is a challenging task in computer vison and numerous image processing applications. For example to detect the corners in an image there exists numerous algorithms. Corners are formed by combining multiple edges and which sometimes may not define the boundary of an image. This paper is mainly concentrates on the study of the Harris corner detection algorithm which accurately detects the corners exists in the image. The Harris corner detector is a widely used interest point detector due to strong features such as rotation, scale, illumination and in the case of noise. It is based on the local auto-correlation function of a signal; where the local auto-correlation function measures the local changes of the signal with patches shifted by a small amount in di?erent directions. In out experiments we have shown the results for gray scale images as well as for color images which gives the results for the individual regions present in the image. This algorithm is more reliable than the conventional methods
Scan registration for autonomous mining vehicles using 3D-NDT
Scan registration is an essential subtask when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalization and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Strasser, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory efficient scan surface representation
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