The efficient integration of visual and tactile information is essential for slip detection and evaluation of grasping stability. However, existing research generally combines visual or tactile modalities as prior information, without fully exploring mechanisms for fusing complementary modalities. In this article, we propose a visual–tactile fusion Transformer (VTF-Trans) for slip detection, designed to handle unaligned data in different formats and facilitate cross-modal information exchange. The main advantages of the proposed method are summarized as follows: first, VTF-Trans employs an improved dual-stream transformer for feature extraction. In addition, we introduce a gated modal attention module to further refine cross-modal fusion. Compared with the existing methods, VTF-Trans effectively integrates useful information from different modalities across multiple scales. Second, to extract deep multimodal information, we propose a cross-modal attention (CMA) mechanism. By defining cross-affinity based on single-modality affinities (token metrics), CMA naturally alleviates the gap between different modalities and domains. Third, we evaluate VTF-trans on three datasets and conduct unknown object grasping experiments. Compared with the state-of-the-art methods, VTF-Trans achieves the highest accuracy for robotic grasping and slip detection, highlighting its superior performance and practical applicability.

High-resolution optical satellite geospatial products play a pivotal role in Earth observation science, as their geopositioning accuracy significantly impacts various geospatial applications. While conventional geometric correction methods rely on high-accuracy ground control points (GCPs), the global scarcity of such reference data has driven the development of GCP-free methodologies. Existing approaches, utilizing public base maps [e.g., Google Earth, shuttle radar topography mission (SRTM)] to generate coarse-accuracy GCPs (CAGCPs), predominantly employ single-temporal references, inherently constrained by the base maps’ intrinsic positioning errors. Although recent attempts to manually extract multitemporal control points have partially reduced random planar errors, these methods critically neglect substantial systematic and gross errors in both elevation and planar dimensions, particularly from off-ground features in urban areas. To further improve geopositioning accuracy using multitemporal base maps, this article introduces a novel method of semantic-enhanced geopositioning refinement (SEGPR), which refines the available CAGCPs through the incorporation of semantic information and error adjustment techniques with multitemporal base maps. Specifically, this article employs a robust method to automatically capture coarse-accuracy matching control points (CAMCPs) using deep features from multitemporal base maps. Subsequently, this article innovatively explores the error characteristics of CAMCPs on both ground and off-ground features. Based on this exploration, semantic-guided random sample consensus (SGRANSAC) is introduced as a coarse correction to address elevation systematic errors, planar projection errors, and gross errors in off-ground MCPs, ensuring reliable MCP selection. In addition, the proposed elevation-augmented weight iteration (EAWI) iteratively reduces the influence of systematic elevation and random planar errors related to ground MCPs, enhancing GCP accuracy, particularly in elevation. The proposed algorithm is evaluated on geometric positioning and modeling accuracy. Experimental results across five global regions demonstrate an average geometric positioning accuracy of 1.42 m, a 55.90% improvement over state-of-the-art nonbasemap methods, and a 50.52% enhancement compared to state-of-the-art basemap methods. All corrected products achieve absolute geometric positioning accuracies better than 2 m, and modeling accuracy improves by 18.37%. The implementation of the proposed algorithm is available athttps://github.com/graduate-2024/SEGPR

Feature matching determines the orientation accuracy for the High Spatial Resolution (HSR) optical satellite stereos, subsequently impacting several significant applications such as 3D reconstruction and change detection. However, the matching of off-track HSR optical satellite stereos often encounters challenging conditions including wide-baseline observation, significant radiometric differences, multi-temporal changes, varying spatial resolutions, inconsistent spectral resolution, and diverse sensors. In this study, we evaluate various advanced feature matching algorithms for HSR optical satellite stereos. Utilizing a specially constructed dataset from five satellites across six challenging scenarios, HSROSS Dataset, we conduct a comparative analysis of four algorithms: the traditional SIFT, and deep-learning based methods including SuperPoint + SuperGlue, SuperPoint + LightGlue, and LoFTR. Our findings highlight overall superior performance of SuperPoint + LightGlue in balancing robustness, accuracy, distribution, and efficiency, showcasing its potential in complex HSR optical satellite scenarios.

Automatic registration of optical and synthetic aperture radar (SAR) images is one of the most challenging tasks due to the influence of speckle noise and nonlinear radiation differences. In this article, we propose a compound registration method for optical and SAR images based on feature decoupling network (FDNet), which consists of a residual denoising network (RDNet) and a pseudo-Siamese fully convolutional network (PSFCN). First, we propose a fast compound matching algorithm, which can overcome the respective weaknesses of the registration accuracy and computational complexity of the feature-based and area-based methods. Specifically, FAST keypoint detection is used to generate the center of the initial template. The extraction of local feature descriptors and the matching of the initial templates are implemented by PSFCN. Second, we design an RDNet to learn the statistical model of speckle noise in SAR images and define a new loss function based on mean-square error (mse) and total variation (TV) to achieve the propagation of speckle noise. PSFCN and RDNet are used to learn deep representations of semantic and noise information, respectively. Then, the semantic and noise features are decoupled on spaced convolutional layers. Finally, the optimal matching templates are searched in a small search window around the initial matching templates. In addition, we propose a strategy for adaptively selecting the template size based on 2-D entropy, which can select the appropriate template size according to the content richness of SAR images. Registration results on a public registration dataset show that our proposed method achieves better performance than other state-of-the-art methods.

We discuss the parallelization of algorithms for solving polynomial systems by way of triangular decomposition. The Triangularize algorithm proceeds through incremental intersections of polynomials to produce different components (points, curves, surfaces, etc.) of the solution set. Independent components imply the opportunity for concurrency. This "component-level" parallelization of triangular decompositions, our focus here, belongs to the class of dynamic irregular parallelism. Potential parallel speed-up depends only on geometrical properties of the solution set (number of components, their dimensions and degrees); these algorithms do not scale with the number of processors. To manage the irregularities of component-level parallelization we combine different concurrency patterns, namely, workpile, producer-consumer, and fork/join. We report on our implementation in the freely available BPAS library. Experimentation with thousands of polynomial systems yield examples with up to 9.5× speed-up on a 12-core machine.

In symbolic computation, polynomial multiplication is a fundamental operation akin to matrix multiplication in numerical computation. We present efficient implementation strategies for FFT-based dense polynomial multiplication targeting multi-cores. We show that balanced input data can maximize parallel speedup and minimize cache complexity for bivariate multiplication. However, unbalanced input data, which are common in symbolic computation, are challenging. We provide efficient techniques, what we call contraction and extension, to reduce multivariate (and univariate) multiplication to balanced bivariate multiplication. Our implementation in Cilk++ demonstrates good speedup on multi-cores.

Given a regular chain T, we aim at finding an efficient way for computing a system of generators of Sat(T), the saturated ideal of T. A natural idea is to test whether the equality {T}=Sat(T) holds, that is, whether T generates its saturated ideal. By generalizing the notion of primitivity from univariate polynomials to regular chains, we establish a necessary and sufficient condition, together with a Grobner basis free algorithm, for testing this equality. Our experimental results illustrate the efficiency of this approach in practice.

Since the discovery of Gröbner bases, the algorithmic advances in Commutative Algebra have made possible to tackle many classical problems in Algebraic Geometry that were previously out of reach. However, algorithmic progress is still desirable, for instance when solving symbolically a large system of algebraic non-linear equations. For such a system, in particular if its solution set consists of geometric components of different dimension (points, curves, surfaces, etc) it is necessary to combine Gröbner bases with decomposition techniques, such as triangular decompositions. Ideally, one would like each of the different components to be produced by an independent processor, or set of processors. In practice, the input polynomial system, which is hiding those components, requires some transformations in order to split the computations into sub-systems and, then, lead to the desired components. The efficiency of this approach depends on its ability to detect and exploit geometrical information during the solving process.Our work addresses two questions: How to discover geometrical information, at an early stage of the solving process, that would be favorable to parallel execution? How to ensure load balancing among the processors? We answer these questions in the context of triangular decompositions [2] which are a popular way of solving polynomial systems symbolically. These methods tend to split the input polynomial system into subsystems and, therefore, are natural candidate for parallel implementation. However, the only such method which has been parallelized so far is the Characteristic Set Method of Wu [5], as reported in [1, 6]. This approach suffers from several limitations. For instance, the solving of the second component cannot start before that of the first one is completed; this is a limitation in view of coarse-grain parallelization.In [4] an algorithm, called Triade, for TRIAngular DEcompositions, provides a good management of the intermediate computations for triangular decompositions. It is also a natural candidate for coarse-grain parallel implementation based on geometrical considerations; indeed the number of working processors can depend on the intrinsic difficulty of the system to solve. However, several challenges remain to be considered. First, load balancing is very difficult to control due to irregular tasks. Even worse: for some input polynomial systems, especially with integer coefficients, resource consuming tasks may not be necessarily executed concurrently. Second, data communication overhead can be very heavy due to large intermediate results.In order to achieve load balancing we rely on the following facts. For an input polynomial system, the Triade algorithm generates the intermediate or output components by decreasing order of dimension. As a consequence, expensive tasks (those in lower dimension) can be processed concurrently. In addition, when solving a (non-trivial) polynomial system modulo a prime integer, the number of these tasks is sufficient for expecting a good speed-up in a parallel execution. The case of polynomial systems with integer coefficients can also benefit from these features by using the modular techniques introduced in [3].We have developed a parallel scheme for the Triade algorithm, aiming at minimizing data communication overhead. Tasks are scheduled and updated by a process manager. Individual tasks are solved "lazily" by process workers. However, each process worker keeps track of enough information such that it can continue the solving of some of these tasks, when needed.We have realized a preliminary implementation on a shared memory multiprocessor. The experimental results show a satisfactory speed-up for some well-known problems.

We present lifting techniques for triangular decompositions of zero-dimensional varieties, that extend the range of the previous methods. We discuss complexity aspects, and report on a preliminary implementation. Our theoretical results are comforted by these experiments. Categories and Subject Descriptors: I.I.2 [Computing