Local Bundle Research Articles

Scaling law in avalanche breaking of composite materials

PurposeThe fibers are loaded by the cosine component of the external load, when a fiber fails, and due to the local load-sharing nature, its force is shared by surviving neighboring fibers. The results show that the system presents a greater resistance and toughness toward the applied load compared to the classical one.Design/methodology/approachIn this paper, the authors adopt the dynamics of a local load-sharing fiber bundle model in two dimensions under an external load to study scaling law in failure process of composite materials with randomly oriented fibers. The model is based on the fiber bundle model where the fibers are randomly oriented. The system is different to the classical one where the fibers are arranged in parallel with the applied load direction.FindingsThe evolution time of the fraction of broken fiber is described by an exponential law with two characteristic times. The latter decrease linearly and exponentially respectively with both applied load and temperature.Originality/valueScaling behavior of the broken fiber numbers with the size system shows that the system exhibits a scaling law of Family–Vicsek model with universal exponents.

Constrained RGBD-SLAM

SUMMARYThis paper introduces a new RGBD-Simultaneous Localization And Mapping (RGBD-SLAM) based on a revisited keyframe SLAM. This solution improves the localization by combining visual and depth data in a local bundle adjustment. Then, it presents an extension of this RGBD-SLAM that takes advantage of a partial knowledge of the scene. This solution allows using a prior knowledge of the 3D model of the environment when this latter is available which drastically improves the localization accuracy. The proposed solutions called RGBD-SLAM and Constrained RGBD-SLAM are evaluated on several public benchmark datasets and on real scenes acquired by a Kinect sensor. The system works in real time on a standard central processing units and it can be useful for certain applications, such as localization of lightweight robots, UAVs, and VR helmet.

Fast incremental structure from motion based on parallel bundle adjustment

Structure from motion has attracted a lot of research in recent years, with new state-of-the-art approaches coming almost every year. One of its advantages over 3D reconstruction is that it can be used for any cameras (UAVs, depth sensor, light field) and produces relatively accurate point clouds and camera parameters. One of its disadvantages compared to other approaches is that it is computationally expensive. In this paper, we design a novel structure-from-motion framework to reduce the computational cost and implement a parallel bundle adjustment on GPU device for large-scale optimization. In our framework, the local bundle adjustment is added into the architecture of the incremental structure from motion; namely, the point clouds and camera’s parameters are optimized when an additional number of images was added. Then, the purpose is not only to improve the quality of the produced point clouds but also to reduce computation time via parallel bundle adjustment. We conduct extensively experiments on several challenging datasets and make comparison with the state-of-the-art methods. Experimental results show that the proposed method has the best performance in terms of accuracy and efficiency.

Hybrid Camera Pose Estimation With Online Partitioning for SLAM

This paper presents a hybrid real-time camera pose estimation framework with a novel partitioning scheme and introduces motion averaging to monocular Simultaneous Localization and Mapping (SLAM) systems. Breaking through the limitations of fixed-size temporal partitioning in many conventional SLAM pipelines, our approach significantly improves the accuracy of local bundle adjustment by gathering spatially-strongly-connected cameras into each block. With the dynamic initialization using intermediate computation values, \XL{we improve the Levenberg-Marquardt solver to further enhance the efficiency of the local optimization.} Moreover, the dense data association between blocks by our co-visibility-based partitioning enables us to explore and implement motion averaging to efficiently align the blocks globally, updating camera motion estimations on-the-fly. Experiments on benchmarks convincingly demonstrate the practicality and robustness of our proposed approach by significantly outperforming conventional approaches.

Autonomous State Estimation and Mapping in Unknown Environments With Onboard Stereo Camera for Micro Aerial Vehicles

Industrial micro aerial vehicles (MAVs) with robotic manipulators have numerous applications in search and rescue tasks that reduce risks to human beings. However, such tasks distinctly require MAVs to have the capability of real-time autonomous navigation only with onboard sensors, especially in GPS-denied applications. This article introduces a new approach to onboard vision-based autonomous state estimation and mapping for MAVs’ navigation in unknown environments. The algorithms run on board and do not need an external positioning system to assist autonomous navigation. The state estimator is developed to provide MAV's current pose on the basis of the extended Kalman filter by using image patch features. Inverse depth convergence monitoring and local bundle adjustment are utilized to improve the accuracy. The mapping algorithm for navigation is developed according to a real-time stereo matching method for three-dimensional perception. Finally, we have performed several experiments to demonstrate the effectiveness of the proposed approach.

ЧИСЛЕННО-ЭКСПЕРИМЕНТАЛЬНОЕ ИССЛЕДОВАНИЕ МНОГОСЛОЙНЫХ ЦИЛИНДРИЧЕСКИХ КОРПУСОВ С ЛОКАЛЬНЫМИ РАССЛОЕНИЯМИ

A numerical and experimental study of the regularities of the influence of the most likely defects such as local bundles inside multilayer cylindrical large-sized housings on the deformation and strength characteristics of the housings near their end connection is carried out. A statistical analysis of discontinuities in the manufacture of holes for pin-pin connection at the front and rear junctions of the joint is carried out. To form a sample, the results of accompanying passports of more than 200 build-ings were processed. Based on the results of ultrasonic flaw detection, the areas of discontinuities at the front and rear junctions of the joint were determined, and as a result of statistical processing, diagrams of the distribution of discontinuities were con-structed. Physical models of cases in the form of multilayered cylindrical rings with the presence of probable defects in the structure such as local bundles are made and, further, to conduct ring tests according to the standard method to study the effect of local bundles on the deformation and strength characteristics of the ring. The relationship between the relative strength limit and the coefficient of discontinuity (delamination) of the composite structure of the ring is established using the obtained exper-imental data. A defect in the form of a bundle, on the one hand, leads to a decrease in the mechanical characteristics of the ma-terial, and on the other hand, leads to a concentration of stresses in the bundle zone. The total effect of stratification is ex-pressed in an increase in the corresponding stresses that characterize the state of the loaded structure, and, accordingly, a de-crease in its load-bearing capacity. A mathematical model is developed and numerical modeling is performed on its basis.

Persistent Stereo Visual Localization on Cross-Modal Invariant Map

Autonomous mobile vehicles are expected to perform persistent and accurate localization with low-cost equipment. To achieve this goal, we propose a stereo camera based visual localization method using a modified laser map, which takes the advantage of both the low cost of camera, and high geometric precision of laser data to achieve long-term performance. Considering that LiDAR and camera give measurements of the same environment in different modalities, the cross-modal invariance is investigated to modify the laser map for visual localization. Specifically, a map learning algorithm is introduced to sample the robust subsets in laser maps that are useful for visual localization using multi-session visual and laser data. Further, a generative map model is derived to describe this cross-modal invariance, based on which two types of measurements are defined to model the laser map points as appropriate visual observations. Tightly coupling these measurements within the local bundle adjustment during online sliding-window based visual odometry, the vehicle can achieve robust localization even one year after the map was built. The effectiveness of the proposed method is evaluated on both the public KITTI datasets and self-collected datasets in our campus, which include seasonal, illumination and object variations. On all experimental localization sessions, our method provides satisfactory results, even when the direction is opposite to that in the mapping session, verifying the superior performance of the laser map based visual localization method.

Fast and robust visual odometry with a low-cost IMU in dynamic environments

PurposeTo realize stable and precise localization in the dynamic environments, the authors propose a fast and robust visual odometry (VO) approach with a low-cost Inertial Measurement Unit (IMU) in this study.Design/methodology/approachThe proposed VO incorporates the direct method with the indirect method to track the features and to optimize the camera pose. It initializes the positions of tracked pixels with the IMU information. Besides, the tracked pixels are refined by minimizing the photometric errors. Due to the small convergence radius of the indirect method, the dynamic pixels are rejected. Subsequently, the camera pose is optimized by minimizing the reprojection errors. The frames with little dynamic information are selected to create keyframes. Finally, the local bundle adjustment is performed to refine the poses of the keyframes and the positions of 3-D points.FindingsThe proposed VO approach is evaluated experimentally in dynamic environments with various motion types, suggesting that the proposed approach achieves more accurate and stable location than the conventional approach. Moreover, the proposed VO approach works well in the environments with the motion blur.Originality/valueThe proposed approach fuses the indirect method and the direct method with the IMU information, which improves the localization in dynamic environments significantly.

Definition of the Spatial Propagator and Implications for Magnetic Field Properties

We present a theoretical framework to analyze the 3D coronal vector magnetic-field structure. We assume that the vector magnetic field exists and is a priori smooth. We introduce a generalized connectivity phase space associated with the vector magnetic field in which the basic elements are the field line and its linearized variation: the Spatial Propagator. We provide a direct formulation of these elements in terms of the vector magnetic field and its spatial derivatives, constructed with respect to general curvilinear coordinates and the equivalence class of general affine parameterizations. The Spatial Propagator describes the geometric organization of the local bundle of field lines, equivalent to the kinematic deformation of a propagated volume tied to the bundle. The Spatial Propagator’s geometric properties are characterized by dilation, anisotropic stretch, and rotation. Extreme singular values of the Spatial Propagator describe quasi-separatrix layers (QSLs), while true separatrix surfaces and separator lines are identified by the vanishing of one and two singular values, respectively. Finally, we show that, among other possible applications, the squashing factor [Q] is easily constructed from an analysis of particular sub-matrices of the Spatial Propagator.

Robust Structure From Motion Based on Relative Rotations and Tie Points

In this article, we present two new approaches for image orientation with a focus on robustness, starting with relative orientations of available image pairs, an incremental and a global one, and compare their performance. For the incremental approach, we first choose a suitable initial image pair, and we then iteratively extend the image cluster by adding new images. The rotations of these newly added images are estimated from relative rotations by single rotation averaging. In the next step, a linear equation system is set up for each new image to solve the translation parameters with triangulated tie points that can be viewed in that new image, followed by a resection for refinement. Finally, we refine the orientation parameters of the images by a local bundle adjustment. We also present a global method that consists of two parts: global rotation averaging, followed by setting up a large linear equation system to solve for all image translation parameters simultaneously; a final bundle adjustment is carried out to refine the results. We compare these two methods by analyzing results on different benchmark sets, including ordered and unordered image data sets from the Internet and two other challenging data sets to demonstrate the performance of our two approaches. We conclude that while the incremental method typically yields results of higher accuracy and performs better on the challenging data sets, our global method runs significantly faster.

Asynchronous Structure from Motion at Scale

Vision systems that provide a 360-degree view are becoming increasingly common in today’s vehicles. These systems are generally composed of several cameras pointing in different directions and rigidly connected to each other. The purpose of these systems is to provide driver assistance in the form of a display, for example by building a Bird’s eye view around the vehicle for parking assistance. In this context, and for reasons of cost and ease of integration, such cameras are generally not synchronized. If non-synchronization is not a problem when it comes to display only, it poses significant issues for more complex computer vision applications (3D reconstruction, motion estimation, etc.). In this article, we propose to use a network of asynchronous cameras to estimate the motion of the vehicle and to find the 3D structure of the scene around it (for example for obstacle detection). Our method relies on the use of at least three images from two adjacent cameras. The poses of the cameras are independently estimated by conventional visual odometry algorithms. Then we show that it is possible to find the absolute scale factor by hypothesizing that the motion of the vehicle is smooth. The results are then refined through a local bundle adjustment on the scale factor and 3D points only. We evaluated our method under real conditions on the KITTI database, and we showed that our method can be generalized to a larger network of cameras thanks to a system developed in our lab.

Time to failure of dynamic local load-sharing fiber bundle models in 1 to 3 dimensions

Extensive Monte Carlo simulations are carried out in one, two and three dimensions for dynamic local load-sharing fiber bundle models following a power-law breaking rule with exponent ρ. This exponent controls the degree of disorder of the bundle. The results are obtained using two methods of introducing disorder in the simulations. In the standard, or classical, Monte Carlo method the disorder is quenched; in the second, or radioactive method the disorder is annealed. Both methods give identical mean time-to-failure values for systems of the same size. However, the radioactive method proves to be more efficient due to the smaller standard deviation of the probability distribution function of the time-to-failure. We take advantage of this efficiency to compute the asymptotic mean time-to-failure of large systems as a function of the degree of disorder, as parameterized by ρ. Based on these extensive simulations, conclusions are drawn regarding the upper critical dimension of time-dependent local load-sharing fiber bundle models.

Bundled Local Features for Image Representation

Local features have been widely used for image representation. Traditional methods often treat each local feature independently or simply model the correlations of local features with spatial partition. However, local features are correlated and should be jointly modeled. Besides, due to the variety of images, predefined partition rules will probably introduce noisy information. To solve these problems, in this paper we propose a novel bundled local features method for efficient image representation and apply it for classification. Specially, we first extract local features and bundle them together with over-complete spatial shapes by viewing each local feature as the central point. Then, the most discriminatively bundling features are selected by reconstruction error minimization. The encoding parameters are then used for image representations in a matrix form. Finally, we train bi-linear classifiers with quadratic hinge loss to predict the classes of images. The proposed method can combine local features appropriately and efficiently for discriminative representations. Experimental results on three image data sets show the effectiveness of the proposed method compared with other local features combination strategies.

Robust RGB-D visual odometry based on edges and points

Localization in unknown environments is a fundamental requirement for robots. Egomotion estimation based on visual information is a hot research topic. However, most visual odometry (VO) or visual Simultaneous Localization and Mapping (vSLAM) approaches assume static environments. To achieve robust and precise localization in dynamic environments, we propose a novel VO based on edges and points for RGB-D cameras. In contrast to dense motion segmentation, sparse edge alignment with distance transform (DT) errors is adopted to detect the states of image areas. Features in dynamic areas are ignored in egomotion estimation with reprojection errors. Meanwhile, static weights calculated by DT errors are added to pose estimation. Furthermore, local bundle adjustment is utilized to improve the consistencies of the local map and the camera localization. The proposed approach can be implemented in real time. Experiments are implemented on the challenging sequences of the TUM RGB-D dataset. The results demonstrate that the proposed robust VO achieves more accurate and more stable localization than the state-of-the-art robust VO or SLAM approaches in dynamic environments.

ROBUST IMAGE ORIENTATION BASED ON RELATIVE ROTATIONS AND TIE POINTS

Abstract. In this paper we present a novel approach for image orientation by combining relative rotations and tie points. First, we choose an initial image pair with enough correspondences and large triangulation angle, and we then iteratively add clusters of new images. The rotation of these newly added images is estimated from relative rotations by single rotation averaging. In the next step, a linear equation system is set up for each new image to solve the translation parameters with triangulated tie points which can be viewed in that new image, followed by a resection for refinement. Finally, we optimize the cluster of reconstructed images by local bundle adjustment. We show results of our approach on different benchmark datasets. Furthermore, we orient several larger datasets incl. unordered image datasets to demonstrate the robustness and performance of our approach.

Nerve Growth Factor Is Associated With Sexual Pain in Women With Endometriosis.

Endometriosis is present in 1 in 10 reproductive-age women, and half experience deep dyspareunia (pelvic pain with sexual intercourse). Our objective was to investigate nerve growth factor (NGF) and its receptors (TrkA/p75NTR) in endometriosis-associated deep dyspareunia. A total of 32 women with endometriosis in the posterior pelvic compartment (cul-de-sac/uterosacrals) were included, either with (n = 17) or without (n = 15) deep dyspareunia symptoms confirmed by endovaginal ultrasound-assisted palpation on examination. Expression of NGF, TrkA, and p75NTR in the surgically excised cul-de-sac/uterosacral endometriosis was examined by immunohistochemistry and Histoscore blinded to pain phenotypes. Additionally, endometriotic stromal cells (ESCs; n = 3) from ectopic endometriosis lesions were cultured and incubated with/without NGF and/or Trk inhibitor K252a, followed by expression analysis of prostaglandin-endoperoxide synthase 2 (PTGS-2)/cyclooxygenase 2 (COX-2; reverse transcription quantitative real-time polymerase chain reaction and Western blot) and prostaglandin E2 (PGE2) secretion (enzyme-linked immunosorbent assay). We found that the immunointensity of NGF and TrkA, but not p75NTR, was significantly elevated in endometriotic stroma and epithelium from women with deep dyspareunia compared to women without deep dyspareunia. Nerve growth factor immunoreactivity in the stroma was also significantly associated with deep dyspareunia intensity and local nerve bundle density. In cultured ESCs, NGF significantly increased PTGS-2/COX-2 mRNA and protein levels as well as PGE2 secretion, and these effects could be abolished by pretreatment of Trk inhibitor K252a. In conclusion, elevated NGF levels were associated with deep dyspareunia in women with cul-de-sac/uterosacral endometriosis. This association may be mediated by increased nerve bundle density and by COX-2/PGE2 stimulation via Trk receptor. Nerve growth factor signaling may play an important role in endometriosis-associated sexual pain.

Portable 3-D modeling using visual pose tracking

This work deals with the passive tracking of the pose of a close-range 3-D modeling device using its own high-rate images in realtime, concurrently with customary 3-D modeling of the scene. This novel development makes it possible to abandon using inconvenient, expensive external trackers, achieving a portable and inexpensive solution. The approach comprises efficient tracking of natural features following the Active Matching paradigm, a frugal use of interleaved feature-based stereo triangulation, visual odometry using the robustified V-GPS algorithm, graph optimization by local bundle adjustment, appearance-based relocalization using a bank of parallel three-point-perspective pose solvers on SURF features, and online reconstruction of the scene in the form of textured triangle meshes to provide visual feedback to the user. Ideally, objects are completely digitized by browsing around the scene; in the event of closing the motion loop, a hybrid graph optimization takes place, which delivers highly accurate motion history to refine the whole 3-D model within a second. The method has been implemented on the DLR 3D-Modeler; demonstrations and abundant video material validate the approach. These types of low-cost systems have the potential to enhance traditional 3-D modeling and conquer new markets owing to their mobility, passivity, and accuracy.

Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy.

.The most effective treatment for twin-to-twin transfusion syndrome is laser photocoagulation of the shared vascular anastomoses in the placenta. Vascular connections are extremely challenging to locate due to their caliber and the reduced field-of-view of the fetoscope. Therefore, mosaicking techniques are beneficial to expand the scene, facilitate navigation, and allow vessel photocoagulation decision-making. Local vision-based mosaicking algorithms inherently drift over time due to the use of pairwise transformations. We propose the use of an electromagnetic tracker (EMT) sensor mounted at the tip of the fetoscope to obtain camera pose measurements, which we incorporate into a probabilistic framework with frame-to-frame visual information to achieve globally consistent sequential mosaics. We parametrize the problem in terms of plane and camera poses constrained by EMT measurements to enforce global consistency while leveraging pairwise image relationships in a sequential fashion through the use of local bundle adjustment. We show that our approach is drift-free and performs similarly to state-of-the-art global alignment techniques like bundle adjustment albeit with much less computational burden. Additionally, we propose a version of bundle adjustment that uses EMT information. We demonstrate the robustness to EMT noise and loss of visual information and evaluate mosaics for synthetic, phantom-based and ex vivo datasets.

3D reconstruction system based on incremental structure from motion using a camera with varying parameters

In this paper, we present a flexible and fast system for multi-scale objects/scenes 3D reconstruction from uncalibrated images/video taken by a moving camera characterized by variable parameters. The proposed system is based on incremental structure from motion and good exploitation of bundle adjustment. At first, from two selected images, our system allows to recover, in a well-chosen reference, coordinates of a set of 3D points. In this context, we have proposed a new method of self-calibration based on the use of two unknown scene points with their image projections. After that, new images are inserted progressively using 3D information already obtained. Local bundle adjustment is used to adjust the new estimated entities. At some time, we introduce a global bundle adjustment to adjust as best as possible all estimated entities and to have an initial 3D model of quality covering an interesting part of the object/scene. This model will be used as reference for the insertion of the rest of images. The proposed system allows to obtain satisfactory results within a reasonable time.

Three dimensional fatigue damage evolution in non-crimp glass fibre fabric based composites used for wind turbine blades

This work studies the tension fatigue damage progression of a uni-directional glass fibre composite made from a non-crimp fabric similar to those used for the main load carrying parts of a wind turbine blade. The spatial damage progression in a chosen region of a test specimen is monitored on a micro-structural scale by ex-situ X-ray computed tomography. The centimetre sized specimen remains uncut during the ex-situ experiment. The experimental results indicate that uni-directional fibre fractures initiate from matrix cracks related to the structure of the fabric: first in the thin off-axis backing bundles at triple cross-over regions where the ±45∘ and 90∘ backing bundles intersect each other and lie close to a uni-directional bundle, and later followed by damage initiation in the other cross-over regions. Uni-directional fibre fractures were seen to increase in number with increasing number of cycles, and mainly progress in the thickness direction of uni-directional bundles (away from the backing bundles). Furthermore, the crack face separation of individual broken uni-directional fibres was observed to gradually increase with an increasing number of cycles. The progression path of the uni-directional fibre fractures was seen to be very dependent on the local backing bundle arrangement.