2,530 research outputs found
Direct Monocular Odometry Using Points and Lines
Most visual odometry algorithm for a monocular camera focuses on points,
either by feature matching, or direct alignment of pixel intensity, while
ignoring a common but important geometry entity: edges. In this paper, we
propose an odometry algorithm that combines points and edges to benefit from
the advantages of both direct and feature based methods. It works better in
texture-less environments and is also more robust to lighting changes and fast
motion by increasing the convergence basin. We maintain a depth map for the
keyframe then in the tracking part, the camera pose is recovered by minimizing
both the photometric error and geometric error to the matched edge in a
probabilistic framework. In the mapping part, edge is used to speed up and
increase stereo matching accuracy. On various public datasets, our algorithm
achieves better or comparable performance than state-of-the-art monocular
odometry methods. In some challenging texture-less environments, our algorithm
reduces the state estimation error over 50%.Comment: ICRA 201
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)
In stereo-vision processing, the image-matching step is essential for results, although it
involves a very high computational cost. Moreover, the more information is processed, the more time
is spent by the matching algorithm, and the more ine cient it is. Spike-based processing is a relatively
new approach that implements processing methods by manipulating spikes one by one at the time
they are transmitted, like a human brain. The mammal nervous system can solve much more complex
problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy
for visual information processing based on the neuro-inspired address-event-representation (AER)
is currently achieving very high performance. The aim of this work was to study the viability of a
matching mechanism in stereo-vision systems, using AER codification and its implementation in
a field-programmable gate array (FPGA). Some studies have been done before in an AER system
with monitored data using a computer; however, this kind of mechanism has not been implemented
directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied
and implemented, with other restrictions, in order to achieve good results in a real-time scenario.
The results and conclusions are shown, and the viability of its implementation is proven.Ministerio de EconomĂa y Competitividad TEC2016-77785-
Projective rectification from the fundamental matrix
This paper describes a direct, self-contained method for planar image rectification of stereo pairs. The method is based solely on an examination of the Fundamental matrix, where an improved method is given for the derivation of two projective transformations that horizontally align all the epipolar projections. A novel approach is proposed to uniquely optimise each transform in order to minimise perspective distortions. This ensures the rectified images resemble the original images as closely as possible. Detailed results show that the rectification precision exactly matches the estimation error of the Fundamental matrix. In tests the remaining perspective distortion offers on average less than one percent viewpoint distortion. Both these factors offer superior robustness and performance compared with existing techniques
Estimating Epipolar Geometry With The Use of a Camera Mounted Orientation Sensor
Context: Image processing and computer vision are rapidly becoming more and more commonplace, and the amount of information about a scene, such as 3D geometry, that can be obtained from an image, or multiple images of the scene is steadily increasing due to increasing resolutions and availability of imaging sensors, and an active research community. In parallel, advances in hardware design and manufacturing are allowing for devices such as gyroscopes, accelerometers and magnetometers and GPS receivers to be included alongside imaging devices at a consumer level.
Aims: This work aims to investigate the use of orientation sensors in the field of computer vision as sources of data to aid with image processing and the determination of a sceneâs geometry, in particular, the epipolar geometry of a pair of images - and devises a hybrid methodology from two sets of previous works in order to exploit the information available from orientation sensors alongside data gathered from image processing techniques.
Method: A readily available consumer-level orientation sensor was used alongside a digital camera to capture images of a set of scenes and record the orientation of the camera. The fundamental matrix of these pairs of images was calculated using a variety of techniques - both incorporating data from the orientation sensor and excluding its use
Results: Some methodologies could not produce an acceptable result for the Fundamental Matrix on certain image pairs, however, a method described in the literature that used an orientation sensor always produced a result - however in cases where the hybrid or purely computer vision methods also produced a result - this was found to be the least accurate.
Conclusion: Results from this work show that the use of an orientation sensor to capture information alongside an imaging device can be used to improve both the accuracy and reliability of calculations of the sceneâs geometry - however noise from the orientation sensor can limit this accuracy and further research would be needed to determine the magnitude of this problem and methods of mitigation
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