Local Frame Research Articles

Unscented Kalman filter for long-distance vessel tracking in geodetic coordinates

This paper describes a unique instantiation of the unscented Kalman filter (UKF) that is particularly well-suited to long-distance surface vessel tracking. The proposed filter leverages a nonlinear process model to predict the position, speed, and heading of targets directly in geodetic coordinates, obviating the need for intermediate coordinate frame transformations, and enabling the use of a simple linear observer. These features differentiate the proposed filter from prior architectures, most of which require the definition of a planar coordinate system in order to describe the linearized state kinematics in an easily differentiable form. Reliance upon local Earth-tangent frames is acceptable in confined operating regions, but incurs substantial estimation error when the target is far from the local origin. It is shown that this error grows with distance-cubed from the local origin. By tracking targets directly in geodetic coordinates, the proposed filter is able to achieve more consistent performance, and a three-fold reduction in computational cost. A series of numerical simulations and field exercises are performed to ground these claims, and verify the efficacy of the proposed filter.

A novel calculation and simulation method on missile killing effect

To accurately describe the killing effect of missiles especially dealing with the small or fast target, a new method for calculating the killing effect of missiles is proposed in this paper. The missile and the target are divided into several basic geometries based on their shapes, and then the equations of these geometries are established in their local frame. The mass center motion equations of the target and the missile are determined in the light of their initial positions and velocities, respectively, and every geometry moves with its center of mass according to its attitude. The equations of the fuze view field and the target can confirm whether the fuze detects the target, and whether the fragment hit the target can be certified by the same approach. This paper also writes a MATLAB simulation program for this calculation method and designs a new algorithm to determine the position relationship of space geometry using existing functions, and the simulation verifies the effectiveness of this method.

HCCG: Efficient high compatibility correspondence grouping for 3D object recognition and 6D pose estimation in cluttered scenes

Recognizing and localizing queried objects in point clouds is a critical technique for robotic manipulation and bin picking tasks. Even though it has been steadily studied, it is still a challenging task for scenes with heavy occlusion and clutter. To copy with this intractable problem, this paper presents a fast and robust 3D object recognition framework, especially an efficient high compatibility correspondence grouping (HCCG) technique achieved by the correspondence ranking, clustering and expanding operations. Utilizing the HCCG technique, we first cluster the compatible correspondences into several high compatibility correspondence groups. Then, for each group, a 6DoF pose hypothesis is generated by using a point pair feature constraints (PPFCs)-based outlier removal module and a local reference frame (LRF)-based pose estimation algorithm. Finally, a robust pose verification operator is carried out to reject the false positive pose hypotheses and pick up the correct target object pose. Our approach is not only suitable for the multi-object recognition task, but also inherently yields the capability of detecting multiple instances thanks to the efficient HCCG technique. Extensive experiments on four challenging datasets show that our approach yields efficient and timely solutions and its advantages are further verified by comparing with the latest methods.

Trilateration Positioning Using Hybrid Camera–LiDAR System with Spherical Landmark Surface Fitting

Navigation in Global Positioning System–denied environments is notoriously difficult for small unmanned aerial vehicles due to reduction of visible satellites and urban canyon multipath interference. Several existing methods can be used for navigating in a constrained environment, but they often require additional specific sensing hardware for a localization solution or only provide local frame navigation. Autonomous systems often include LiDAR and RGB cameras for mapping, sensing, or obstacle avoidance. Utilizing these sensors for navigation could provide the only or complimentary localization solutions to other Global Positioning System–denied localization methods in a global or local frame, especially in urban canyons where unique landmarks can be identified. Information from scanning LiDAR can be correlated with camera pixel coordinates and used to range unique visual landmarks that have known locations. The present work included surface function fitting to reduce ranging error to spherical landmarks since multiple lasers were able to range each landmark. Simulation and experimental validation of the unique camera–LiDAR modified trilateration process was undertaken using colored light orbs as landmarks with a 16-laser scanning LiDAR and known positions. Position error was computed and verified that the position estimate process was successful at varying landmark configurations and viewing angles in simulation. Experimental results verified the process while also providing higher accuracy than a previous method of using a single point on landmark surfaces, for the tested setup.

A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking

Algorithms for multiagent systems to locate a source or to follow a desired level curve of spatially distributed scalar fields generally require sharing field measurements among the agents for gradient estimation. Yet, in this article, we propose a distributed active perception strategy that enables swarms of various sizes and graph structures to perform source seeking and level curve tracking without the need to explicitly estimate the field gradient or explicitly share measurements. The proposed method utilizes a consensus-like principal component analysis perception algorithm that does not require explicit communication in order to compute a local body frame. This body frame is used to design a distributed control law where each agent modulates its motion based only on its instantaneous field measurement. Several stability results are obtained within a singular perturbation framework that justifies the convergence and robustness of the strategy. Additionally, efficiency is validated through robots experiments.

Gradient-based bearing-only formation control: An elevation angle approach

This paper proposes an elevation angle rigidity theory in both 2D and 3D spaces, and applies it to solve multi-agent formation control with only inter-agent bearing/direction measurements in agents’ local coordinate frames. Motivated by the sensor technology in measuring elevation angle and angular diameter, we develop elevation angle rigidity by attaching each agent in a multi-agent framework with a rod in 2D and a ball in 3D, respectively. By defining the elevation angle rigidity matrix, conditions for infinitesimal elevation angle rigidity are derived. Compared to previously developed angle rigidity-based and bearing rigidity-based formation control laws, the proposed elevation angle rigidity-based control law can maintain the gradient-based control property. Compared to distance-based formation control laws, less sensor measurements are required. The formation maneuvering with desired translation and rotation is also realized by using only local bearing measurements. Simulation examples illustrate the advantages and effectiveness of the proposed approach.

On invariance of spatial isogeometric Timoshenko–Ehrenfest beam formulations for static analysis

This study elaborates the invariance of spatial Timoshenko–Ehrenfest​ beam formulations in the context of isogeometric analysis. Such invariance confirms that zero strain measures are always generated by rigid transformations, i.e., rigid translations and rotations. The violation of this property can degrade the performance of the formulations in predicting structural responses. In the setting of linear analysis, the invariance of planar beam formulations has already been studied, but a similar investigation for spatial beam formulations is not yet carried out. Most of the spatial beam formulations are developed in the local coordinate frame, and components of unknown kinematics in this frame are discretized by using rational B-spline basis functions. Unfortunately, those formulations are found to be non-invariant under such a discretization scheme, and the degradation in their performance is demonstrated. On the other hand, the local coordinate frame is widely defined by the so-called natural Frenet–Serret frame. The sole utilization of this frame does not allow the consideration of beams having twisting configuration. In this study, these shortcomings are resolved.

The Pacific Community Filmmaking Consortium: producing pacific community-based films by Pacific filmmakers

ABSTRACT This case study will discuss the impact pathways associated with a practice-led impact and engagement project based in the Pacific through the establishment of The Pacific Community Filmmaking Consortium. The project aims to support community-based and participatory media production as a creative and credible approach to influencing policy and communities in relation to gender inequality and gender-based violence in the region. The project commissioned and produced a slate of eight films, Film4Gender, by Pacific filmmakers on the theme of gender-based violence, gender inequality and Pacific responses to the Covid-19 pandemic. The project aims to give visibility to and build capacity amongst a network of Pacific community-based filmmakers and organisations whose work with communities addresses gender inequality in vernacular terms, with local participation and frames of reference. Supporting their work opens up alternative routes to understanding and influencing gender inequality in the Pacific. The Consortium’s approach to practice aims to foreground the potential impact of processes of production and distribution of Pacific-made films on individuals, institutions and communities. Drawing on interviews with the filmmakers, this paper demonstrates how some of the project’s first-year impact pathways reflect broader concerns of sustainability of practice and development in the region.

A man never cries: barriers to holistic care for male survivors of sexual violence in eastern DRC

ABSTRACT While we know that most male survivors of conflict-related sexual violence in the Democratic Republic of the Congo (DRC) do not have access to care, little attention has been devoted to a systematic analysis of why this is so. Drawing from semi-structured interviews with staff of service providers and male survivors of sexual violence, as well as from focus group discussions with community members in eastern DRC, this article sets out to explore challenges and barriers related to meeting the needs of male survivors of sexual violence with respect to their medical, psychological, socioeconomic and legal needs. Our findings suggest that local framings of masculinity can both negatively and positively influence support-seeking behaviour depending on how survivors themselves engage with masculinity ideals. Based on these findings, a conceptual framework including seven levels of barriers to care for male survivors has been developed. Although service providers strive to provide high-quality care to male survivors, we show that existing responses to sexual violence have mainly been designed to address sexual violence against women and need to be re-adapted to male survivors.

Rehashed Liberalism, the Accusation of Radical Purity, and the Alibi of the “Personal”

Rehashed Liberalism, the Accusation of Radical Purity, and the Alibi of the “Personal”

G3DOA: Generalizable 3D Descriptor With Overlap Attention for Point Cloud Registration

Point cloud registration (PCR) is a key problem for robotics, autonomous driving, and other applications. Constructing generalizable 3D descriptors and determining whether a 3D descriptor is in the overlapping area are challenging tasks in PCR. Despite the fast evolution of learning-based 3D descriptors, existing methods are either sensitive to rigid transformation and scenario changes, or can not find the appropriate descriptors in the overlapping area, which means their generalization and descriptive ability are not enough for practice applications. To solve these problems, we propose a novel neural network, named G3DOA, which jointly learns generalizable rotation-invariant 3D descriptors and their overlap scores (representing the probability in the overlapping area), to enhance the generalization ability of the descriptors across different data collected by various laser sensors. To ensure the rotation and scale invariance of the point cloud in the input stage, we estimate the Local Reference Frame (LRF) of local patches and normalize the coordinates. To learn generalizable and distinctive descriptors, we propose a novel Cylindrical LRF convolution module with multi-scaled cylindrical shells and neural layers, which hierarchically encodes and aggregates the geometric information in different cylindrical shells. Moreover, to estimate the probability of whether a point is in the overlapping area, we propose an overlap attention module that extracts co-contextual information between the feature encodings of the two point clouds. The experiments show that G3DOA trained only on an indoor dataset can be efficiently generalized to complex outdoor datasets, and the generalization ability of G3DOA outperforms state-of-the-art learning-based 3D descriptors.

Violent farmer–herder conflicts in Ghana: constellation of actors, citizenship contestations, land access and politics

ABSTRACT This paper presents evidence from two cases in Ghana (Agogo and Gushiegu) to examine why farmer–herder conflicts escalate into violence. It argues that aside from resource competition, and crop damage, there are multi-faceted and dynamic processes and factors involved. The cases show that such violent conflicts can be explained by a constellation of actors, politics, land access and citizenship contestations. Many actors are important in this escalation. Local citizenship discourses and framing of conflicts are intricately linked to struggles over access to land and resources through which actors mobilize for violence. Both farmers and herders see cattle owners, chiefs, politicians, local groups and government officials as responsible for mobilizing and inciting violence and clandestinely using politics and citizenship in access to resources. At the same time, farmer–herder conflicts generate internal struggles between those who have interests in cattle (herders/businessmen and chiefs/elders) and those who have no such interests (young first-comer “cattleless” farmers).

Peace in the ground: How land degradation in the Sahel impacts Europe and what the EU can do about it

The deterioration of security and humanitarian conditions in the Sahel region has widely acknowledged implications for the EU, and the strategic importance of tackling them has been established in EU strategies. Local land degradation sits at a nexus of the many challenges as it is a driver of poverty, famine, conflict, migration, poor governance, loss of biodiversity and climate change. A local framing of the issues makes it possible to identify the actions that can address them. The vast number of people engaged in land-based livelihoods offers the potential to halt and even reverse degradation. The main limiting factor for this is the lack of targeted financing. The EU can be a leader in this process through (1) integrating ecosystem health into existing programming; (2) designing new projects targeting sustainable land use; (3) supporting the development of monitoring systems that enable funding, including from carbon markets; and (4) lowering administrative barriers to partnerships.

An Arbitrary Lagrangian–Eulerian Formulation of Two-Dimensional Viscoelastic Beams Based on the Consistent Corotational Method

Abstract In this paper, an arbitrary Lagrangian–Eulerian (ALE) formulation based on the consistent corotational method is presented for the geometric nonlinear dynamic analysis of two-dimensional (2D) viscoelastic beams. In the ALE description, mesh nodes can be moved in some arbitrarily specified way, which is convenient for investigating problems with moving boundaries and loads. By introducing a corotational frame, the rigid-body motion of an element can be removed. Then, the pure deformation and the deformation rate of the element can be measured in the local frame. This method can avoid rigid-body motion damping. In addition, the elastic force vector, the inertia force vector, and the internal damping force vector are derived with the same shape functions to ensure the consistency and independence of the element. Therefore, different assumptions can be made to describe the local deformation of the element. In this paper, the interdependent interpolation element (IIE) and the Kelvin–Voigt model are introduced in the local frame to consider the shear deformation, rotary inertia, and viscoelasticity. Moreover, the presented method is capable of considering the arbitrary curved initial geometry of a beam. Numerical examples show that internal damping dampens only the pure elastic deformation of the beam but does not dampen the rigid-body motion. Three dynamic problems of a beam with a moving boundary or subjected to a moving load are investigated numerically by the presented formulation and the commercial software ansys to verify the validity, versatility, and computational efficiency of the presented formulation.

Special stereotactic radiotherapy techniques: procedures and equipment for treatment simulation and dose delivery.

Stereotactic radiotherapy (SRT ) is a multi-step procedure with each step requiring extreme accuracy. Physician-dependent accuracy includes appropriate disease staging, multi-disciplinary discussion with shared decision-making, choice of morphological and functional imaging methods to identify and delineate the tumor target and organs at risk, an image-guided patient set-up, active or passive management of intra-fraction movement, clinical and instrumental follow-up. Medical physicist-dependent accuracy includes use of advanced software for treatment planning and more advanced Quality Assurance procedures than required for conventional radiotherapy. Consequently, all the professionals require appropriate training in skills for high-quality SRT.Thanks to the technological advances, SRT has moved from a “frame-based” technique, i.e. the use of stereotactic coordinates which are identified by means of rigid localization frames, to the modern “frame-less” SRT which localizes the target volume directly, or by means of anatomical surrogates or fiducial markers that have previously been placed within or near the target. This review describes all the SRT steps in depth, from target simulation and delineation procedures to treatment delivery and image-guided radiation therapy. Target movement assessment and management are also described.

Intelligent Intersection Vehicle and Pedestrian Detection Based on Convolutional Neural Network

The preprocessed images are input to a pretrained neural network to obtain the corresponding feature mapping, and the corresponding region of interest is set for each point in the feature mapping to obtain multiple candidate feature regions; subsequently, these candidate feature regions are fed into a region proposal network and a deep residual network for binary classification and BB regression, and some of the candidate feature regions are filtered out, and the remaining feature regions are subjected to ROIAIign operation; finally, classification, BB regression, and mask generation are performed on these feature regions, and full convolutional nerve network operation is performed in each feature region and output. To further identify the specific model of the vehicle, this paper proposes a multifeature model recognition method that fuses the improved model with the optimized Mask R-CNN algorithm. A vehicle local feature dataset including vehicle badges, lights, air intake grille, and whole vehicle outline is established to simplify the network structure of model. Meanwhile, its detection frame generation process and the adjustment rules of overlapping frame confidence in nonmaximum suppression are improved for coarse vehicle localization. Then, the generated vehicle detection frames after localization are output to the Mask R-CNN algorithm after further optimizing the RPN structure. The localized vehicle detection frames are then output to the Mask R-CNN algorithm after further optimization of the RPN structure for local feature recognition, and good recognition results are achieved. Finally, this paper establishes a distributed server-based vehicle recognition system, which mainly includes database module, file module, feature extraction and matching module, message queue module, WEB module, and vehicle detection module. Due to the limitations of traditional region generation methods, this paper provides a brief analysis of the region generation network in the Faster R-CNN algorithm and details the loss calculation principle of the output layer.

Collision avoidance control for formation flying of multiple spacecraft using artificial potential field

• Trajectory design/control for spacecraft formation flying with obstacle avoidance is presented. • Rotational potential function for obstacle avoidance in 3-dimensional space is newly proposed. • The controller is proven to be asymptotically stable in the sense of Lyapunov and successfully tested. This study presents trajectory design/control for spacecraft formation flying with obstacle avoidance. Based on the artificial potential field (APF), a formation potential is first defined to derive a formation control law for virtual structure, which enables multiple spacecraft to maintain polygonal or tetrahedral formation. As an efficient method to circumvent local minima which often occur in the APF-based approach, a newly proposed rotational potential is derived in a local coordinate frame to add in the APF framework. The synthesized formation and rotational potential function is used to develop a gradient-based control law to design/control the formation flying trajectory while avoiding collision with obstacles. Proven to be asymptotically stable in the sense of Lyapunov, the proposed continuous feedback control law is demonstrated via formation keeping/reconfiguration examples. The proposed approach successfully maintains the trajectory in the desired formation without colliding with obstacles and without falling into local minimum. These results are comparatively analysed with those of other APF-based approaches. The overall analysis shows that the proposed rotational potential, which has been newly derived in this research, enables a group of spacecraft in formation to efficiently avoid collision with obstacles without convergence to a local minimum.

Thermalization of nuclear matter in heavy-ion collisions at Fermi energies

We analyze the time evolution of the kinetic properties of nuclear matter produced in heavy-ion collisions at Fermi energies. The collision system is simulated using Constrained Molecular Dynamics (CoMD) transport calculations whose output is the isospin, position, and momentum of the nucleons. Focusing on central 35A⋅MeV 40Ca+40Ca collisions we utilize this information to extract localized momentum distributions in volume elements of 8fm3 and time steps of 5fm/c. We then parameterize the single-particle momentum distributions with thermally motivated fit functions in the local rest frame of each cell. While the transverse-momentum distributions are well reproduced by thermal ones, the longitudinal ones carry a marked imprint of the initial nuclear motion which we capture by introducing a centroid motion into our fit functions. In particular, we find that Fermi distributions yield significantly better fits than Boltzmann ones, a consequence of the Pauli blocking implemented in CoMD. From the fits we extract the time dependence of the thermodynamic and collective properties of the excited nuclear medium. We find that the transverse temperature gradually rises to about 6MeV, which is accompanied by a dissipation of the initial centroid motion of the incoming nuclei which vanishes at about 100fm/c after initial impact. We are therefore able to track the transition of beam energy into random kinetic energy for nucleons, suggesting a three-dimensional equilibration of energy in the late stages of the collision.

Assembly Configuration Representation and Kinematic Modeling for Modular Reconfigurable Robots Based on Graph Theory

A Modular Reconfigurable Robot (MRR) composed of standard joint and link modules has the advantages of rapid changeover of assembly configurations for a diversity of application requirements. To tackle the kinematics analysis issues for an MRR, it is essential to develop a generic method to mathematically represent all possible assembly configurations and automatically generate their kinematic models. In this paper, two types of robot modules, i.e., revolute joint modules and rigid link modules, are considered as the basic building blocks of an MRR. The topological structure of an MRR is represented by a graph, termed an Assembly Graph (AG), in which all robot modules, regardless of their types, are treated as vertices, while the connecting interfaces between any two robot modules are treated as edges. Based on graph theory, a modified adjacency matrix, termed an Assembly Adjacency Matrix (AAM), is proposed to represent the assembly configuration of an MRR, in which the three-dimensional assembly information is specified with connecting port vectors for the adjacent modules. A path matrix derived from the AAM of an MRR is employed to describe the connecting sequence of its constituting modules. The local frame representation of the Product-of-Exponentials (POE) formula is employed, which is configuration-independent. Therefore, for an MRR configuration represented with an AAM, its kinematic model will be automatically generated according to its path matrix. The proposed assembly configuration representation and kinematic modeling method are validated through a dual-branch MRR configuration.

MDCS with fully encoding the information of local shape description for 3D Rigid Data matching

Local feature description is the fundamental research topic for 3D rigid data matching. However, how to achieve a good balanced performance of the local shape descriptor among descriptiveness, robustness, compactness and efficiency remains a challenging task. For this purpose, we propose a novel feature representation of 3D local surface called multi-view depth and contour signatures (MDCS). Key to MDCS descriptor is multi-view and multi-attribute description to provide a comprehensive and effective geometric information. Specifically, we first construct a repeatable Local Reference Frame (LRF) for the local surface to achieve rotation invariance. Then we integrate the depth information characterized in a local coordinate manner and the 2D contour cue derived from 3D-to-2D projection, forming the depth and contour signatures (DCS). Finally, MDCS is generated by concatenating all the DCS descriptors captured from three orthogonal view planes in the LRF into a vector. The performance of the MDCS method is evaluated on several data modalities (i.e., LiDAR, Kinect, and Space Time) with respect to Gaussian noise, varying mesh resolutions, clutter and occlusion. Experimental results and rigorous comparisons with the state-of-the-arts validate that our approach achieves the superior performance in terms of descriptiveness, robustness, compactness and efficiency. Moreover, we further demonstrate the feasibility of MDCS in matching of both LiDAR and Kinect point clouds for 3D vision applications and evaluate the generalization ability of the proposed method on real-world datasets.