Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and sha...
Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities. However, depth maps are generally of a relatively lower quality with much stronger noise compared to RGB images, making it challenging to acquire grasp depth and fuse multi-modal clues. To address the two issues, this paper proposes a novel learning based approach to RGB-D grasp detection, namely Depth Guided Cross-modal Attention Network (DGCAN). To better leverage the geometry information recorded in the depth channel, a complete 6-dimensional rectangle representation is adopted with the grasp depth dedicatedly considered in addition to those defined in the common 5-dimensional one. The prediction of the extra grasp depth substantially strengthens feature learning, thereby leading to more accurate results. Moreover, to reduce the negative impact caused by the discrepancy of data quality in two modalities, a Local Cross-modal Attention (LCA) module is designed, where the depth features are refined according to cross-modal relations and concatenated to the RGB ones for more sufficient fusion. Extensive simulation and physical evaluations are conducted and the experimental results highlight the superiority of the proposed approach.
Dynamic Facial Expression Recognition (DFER) is crucial for affective computing but often overlooks the impact of scene context. We have identified a significant issue in current DFER tasks: human annotators typically...
Drones are becoming essential tools in emergency situations such as search and rescue, surveillance, and firefighting. These applications make the development of trustworthy software a priority to increase efficiency,...
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ISBN:
(数字)9798331516239
ISBN:
(纸本)9798331516246
Drones are becoming essential tools in emergency situations such as search and rescue, surveillance, and firefighting. These applications make the development of trustworthy software a priority to increase efficiency, cut costs, and reduce risks, in future replacing human personnel in challenging areas. RoboChart, a platform-agnostic tool, brings numerous benefits to the robotics community by providing a language and high-level tool to describe a model via automatic verification and exhaustive testing of the model. However, when it comes to real robotics it is essential to use testing tools suitable for specific robotic software. In the context of the Robotic Operating System (ROS), it imposes its own design constraints, best practices, and communication requirements. This paper aims to apply the RoboChart modelling approach to autonomous firefighting drones, putting forward a low-level ROS software architecture that aligns with RoboChart models, ensuring the trustworthiness of the system during operation.
This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plan...
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In recent years, LiDAR-based localization and mapping methods have achieved significant progress thanks to their reliable and real-time localization capability. However, single LiDAR odometry often faces hardware fail...
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ISBN:
(数字)9798331518493
ISBN:
(纸本)9798331518509
In recent years, LiDAR-based localization and mapping methods have achieved significant progress thanks to their reliable and real-time localization capability. However, single LiDAR odometry often faces hardware failures and degradation in practical scenarios, and the continuous-time measurement characteristic is constantly neglected by existing LiDAR odometry. This motivates us to develop a continuous-time Multi-LiDAR Odometry (MLO) method, namely CT-MLO, which can realize accurate and real-time state estimation using multi-LiDAR measurements through a continuous-time perspective. Due to the advantageous continuous-time formulation, each LiDAR point in a point stream can query the corresponding continuous-time trajectory within its time instants. Additionally, a decentralized multi-LiDAR synchronization scheme is devised to combine points from separate LiDARs into a single point cloud without the need for primary LiDAR assignment. With the detailed derivation of the analytic Jacobians for continuous-time LiDAR observation, the proposed method integrates synchronization, continuous-time estimation, and voxel map management within a Kalman filter framework, which can achieve real-time state estimation with only a few linear iterations. The effectiveness of the proposed method is demonstrated through various scenarios, including public datasets and real-world autonomous driving experiments. The results demonstrate that the proposed CT-MLO can achieve high-accuracy continuous-time state estimations in real-time and is demonstratively competitive compared to other State-of-the-Art (SOTA) methods.
To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5th and 6th polynomials. By imposing corr...
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Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. Th...
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ISBN:
(数字)9798350364132
ISBN:
(纸本)9798350364149
Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model’s parameters but also necessitates the provision of the image’s modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image’s modality to be known. Extensive experiments demonstrate CPL-CLIP’s superiority over existing methods on several flexible-modal FAS benchmarks.
Metasurfaces offer remarkable control over different characteristics of the electromagnetic waves. They can be used to modify the phase, amplitude, polarization, and direction of reflection associated with an incoming...
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ISBN:
(数字)9789463968119
ISBN:
(纸本)9798350359497
Metasurfaces offer remarkable control over different characteristics of the electromagnetic waves. They can be used to modify the phase, amplitude, polarization, and direction of reflection associated with an incoming incident field. This behavior can be mathematically represented using the generalized sheet transition conditions (GSTCs) (K. Achouri and C. Caloz, Electromagnetic Metasurfaces: Theory and Applications, Wiley, 2021). GSTCs connect the electromagnetic fields on the two sides of the sheet using equivalent bianisotropic electric and magnetic susceptibility tensors. These tensors account for the cumulative electric and magnetic polarization density effect of the unit-cell configurations on the electromagnetic fields.
Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple...
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In the era of Industry 4.0, machines are performing their assigned tasks smartly by using intelligent thinking approach. With the advancement in artificial intelligence-based algorithms, the machines are executing the...
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