60,200 research outputs found
Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments
Most approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. In order to effectively combine both types of features, their associated errors are weighted according to their covariance matrices, computed from the propagation of Gaussian distribution errors in the sensor measurements. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech. Project "PROMOVE: Advances in mobile robotics for promoting independent life of elders", funded by the Spanish Government and the "European Regional Development Fund ERDF" under contract DPI2014-55826-R
Fast multi-image matching via density-based clustering
We consider the problem of finding consistent matches
across multiple images. Previous state-of-the-art solutions
use constraints on cycles of matches together with convex
optimization, leading to computationally intensive iterative
algorithms. In this paper, we propose a clustering-based
formulation. We first rigorously show its equivalence with
the previous one, and then propose QuickMatch, a novel
algorithm that identifies multi-image matches from a density
function in feature space. We use the density to order the
points in a tree, and then extract the matches by breaking this
tree using feature distances and measures of distinctiveness.
Our algorithm outperforms previous state-of-the-art methods
(such as MatchALS) in accuracy, and it is significantly faster
(up to 62 times faster on some bechmarks), and can scale to
large datasets (with more than twenty thousands features).Accepted manuscriptSupporting documentatio
Unsupervised Learning of Edges
Data-driven approaches for edge detection have proven effective and achieve
top results on modern benchmarks. However, all current data-driven edge
detectors require manual supervision for training in the form of hand-labeled
region segments or object boundaries. Specifically, human annotators mark
semantically meaningful edges which are subsequently used for training. Is this
form of strong, high-level supervision actually necessary to learn to
accurately detect edges? In this work we present a simple yet effective
approach for training edge detectors without human supervision. To this end we
utilize motion, and more specifically, the only input to our method is noisy
semi-dense matches between frames. We begin with only a rudimentary knowledge
of edges (in the form of image gradients), and alternate between improving
motion estimation and edge detection in turn. Using a large corpus of video
data, we show that edge detectors trained using our unsupervised scheme
approach the performance of the same methods trained with full supervision
(within 3-5%). Finally, we show that when using a deep network for the edge
detector, our approach provides a novel pre-training scheme for object
detection.Comment: Camera ready version for CVPR 201
High-Performance and Tunable Stereo Reconstruction
Traditional stereo algorithms have focused their efforts on reconstruction
quality and have largely avoided prioritizing for run time performance. Robots,
on the other hand, require quick maneuverability and effective computation to
observe its immediate environment and perform tasks within it. In this work, we
propose a high-performance and tunable stereo disparity estimation method, with
a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can
potentially enable robots to quickly reconstruct their immediate surroundings
and maneuver at high-speeds. Our key contribution is a disparity estimation
algorithm that iteratively approximates the scene depth via a piece-wise planar
mesh from stereo imagery, with a fast depth validation step for semi-dense
reconstruction. The mesh is initially seeded with sparsely matched keypoints,
and is recursively tessellated and refined as needed (via a resampling stage),
to provide the desired stereo disparity accuracy. The inherent simplicity and
speed of our approach, with the ability to tune it to a desired reconstruction
quality and runtime performance makes it a compelling solution for applications
in high-speed vehicles.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 2016; 8 pages, 5 figure
Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications
In this paper we present a semi-automatic 2D-3D local registration pipeline
capable of coloring 3D models obtained from 3D scanners by using uncalibrated
images. The proposed pipeline exploits the Structure from Motion (SfM)
technique in order to reconstruct a sparse representation of the 3D object and
obtain the camera parameters from image feature matches. We then coarsely
register the reconstructed 3D model to the scanned one through the Scale
Iterative Closest Point (SICP) algorithm. SICP provides the global scale,
rotation and translation parameters, using minimal manual user intervention. In
the final processing stage, a local registration refinement algorithm optimizes
the color projection of the aligned photos on the 3D object removing the
blurring/ghosting artefacts introduced due to small inaccuracies during the
registration. The proposed pipeline is capable of handling real world cases
with a range of characteristics from objects with low level geometric features
to complex ones
Regularized pointwise map recovery from functional correspondence
The concept of using functional maps for representing dense correspondences between deformable shapes has proven to be extremely effective in many applications. However, despite the impact of this framework, the problem of recovering the point-to-point correspondence from a given functional map has received surprisingly little interest. In this paper, we analyse the aforementioned problem and propose a novel method for reconstructing pointwise correspondences from a given functional map. The proposed algorithm phrases the matching problem as a regularized alignment problem of the spectral embeddings of the two shapes. Opposed to established methods, our approach does not require the input shapes to be nearly-isometric, and easily extends to recovering the point-to-point correspondence in part-to-whole shape matching problems. Our numerical experiments demonstrate that the proposed approach leads to a significant improvement in accuracy in several challenging cases
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