3,511 research outputs found
Assessment of a photogrammetric approach for urban DSM extraction from tri-stereoscopic satellite imagery
Built-up environments are extremely complex for 3D surface modelling purposes. The main distortions that hamper 3D reconstruction from 2D imagery are image dissimilarities, concealed areas, shadows, height discontinuities and discrepancies between smooth terrain and man-made features. A methodology is proposed to improve automatic photogrammetric extraction of an urban surface model from high resolution satellite imagery with the emphasis on strategies to reduce the effects of the cited distortions and to make image matching more robust. Instead of a standard stereoscopic approach, a digital surface model is derived from tri-stereoscopic satellite imagery. This is based on an extensive multi-image matching strategy that fully benefits from the geometric and radiometric information contained in the three images. The bundled triplet consists of an IKONOS along-track pair and an additional near-nadir IKONOS image. For the tri-stereoscopic study a densely built-up area, extending from the centre of Istanbul to the urban fringe, is selected. The accuracy of the model extracted from the IKONOS triplet, as well as the model extracted from only the along-track stereopair, are assessed by comparison with 3D check points and 3D building vector data
Visual SLAM for flying vehicles
The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments. © 2008 IEEE
Reliability in Constrained Gauss-Markov Models: An Analytical and Differential Approach with Applications in Photogrammetry
This report was prepared by Jackson Cothren, a graduate research associate in the Department of Civil and Environmental Engineering and Geodetic Science at the Ohio State University, under the supervision of Professor Burkhard Schaffrin.This report was also submitted to the Graduate School of the Ohio State University as a dissertation in partial fulfillment of the requirements for the Ph.D. degree.Reliability analysis explains the contribution of each observation in an estimation model
to the overall redundancy of the model, taking into account the geometry of the network
as well as the precision of the observations themselves. It is principally used to design
networks resistant to outliers in the observations by making the outliers more detectible
using standard statistical tests.It has been studied extensively, and principally, in Gauss-
Markov models. We show how the same analysis may be extended to various
constrained Gauss-Markov models and present preliminary work for its use in
unconstrained Gauss-Helmert models. In particular, we analyze the prominent reliability
matrix of the constrained model to separate the contribution of the constraints to the
redundancy of the observations from the observations themselves. In addition, we make
extensive use of matrix differential calculus to find the Jacobian of the reliability matrix
with respect to the parameters that define the network through both the original design
and constraint matrices. The resulting Jacobian matrix reveals the sensitivity of
reliability matrix elements highlighting weak areas in the network where changes in
observations may result in unreliable observations. We apply the analytical framework to
photogrammetric networks in which exterior orientation parameters are directly observed
by GPS/INS systems. Tie-point observations provide some redundancy and even a few
collinear tie-point and tie-point distance constraints improve the reliability of these
direct observations by as much as 33%. Using the same theory we compare networks in
which tie-points are observed on multiple images (n-fold points) and tie-points are
observed in photo pairs only (two-fold points). Apparently, the use of two-fold tiepoints
does not significantly degrade the reliability of the direct exterior observation
observations. Coplanarity constraints added to the common two-fold points do not add
significantly to the reliability of the direct exterior orientation observations. The
differential calculus results may also be used to provide a new measure of redundancy
number stability in networks. We show that a typical photogrammetric network with n-fold
tie-points was less stable with respect to at least some tie-point movement than an
equivalent network with n-fold tie-points decomposed into many two-fold tie-points
A new straight line reconstruction methodology from multi-spectral stereo aerial images
In this study, a new methodology for the reconstruction of line features from multispectral stereo aerial images is presented. We take full advantage of the existing multispectral information in aerial images all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The line to line correspondences between the stereo images are established using a new pair-wise stereo matching approach. The approach involves new constraints, and the redundancy inherent in pair relations gives us a possibility to reduce the number of false matches in a probabilistic manner. The methodology is tested over three different urban test sites and provided good results for line matching and reconstruction
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
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