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Challenges for Monocular 6D Object Pose Estimation in robotics

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arXiv 2023年

作者： Thalhammer, Stefan Bauer, Dominik Hönig, Peter Weibel, Jean-Baptiste García-Rodríguez, José Vincze, Markus The Industrial Engineering Departent UAS Technikum Vienna Austria The Automation and Control Institute TU Wien Austria The Columbia Artificial Intelligence and Robotics Lab Columbia University United States The Department of Computer Technology University of Alicante Spain

Object pose estimation is a core perception task that enables, for example, object manipulation and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference make monocular approaches especially well suited for robotics applications. We observe that previous surveys on establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved. © 2023, CC BY.

关键词： Robots

Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review

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arXiv 2024年

作者： Banos, Oresti Comas-González, Zhoe Medina, Javier Polo-Rodríguez, Aurora Gil, David Peral, Jesús Amador, Sandra Villalonga, Claudia Department of Computer Engineering Automation and Robotics University of Granada Granada Spain Department of Computer Science and Electronics Universidad de la Costa Barranquilla Colombia Department of Computer Science University of Jaén Jaén Spain Department of Computer Technology and Computation University of Alicante Alicante Spain Department of Sotware and Computing Systems University of Alicante Alicante Spain

Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather

关键词： Emotion Recognition

Multi-Objective Optimization Design for DC-Link Capacitor of Motor Driver

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Multi-Objective Optimization Design for DC-Link Capacitor of...

Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Qian Liu Kangxing Huang Xueliang Wei Yihong Zheng Hao Xiang School of Automation Wuhan University of Technology Wuhan China Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Hongkong China College of Artificial Intelligence China University of Petroleum-Beijing Beijing China Software Development Department Wuhan iActuator Robotics Co. Ltd Wuhan China

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

The capacitor is a common weak point in motor drive system design, necessitating targeted selection, design, and optimization of capacitor banks within these systems. Traditional capacitor design often relies on engineering experience, usually meeting specific capacitance and voltage requirements through simple parallel or series connections of capacitors. This approach often fails to fully optimize volume, cost, and reliability. This paper uses comprehensive theoretical modeling to optimize capacitor module design, effectively balancing capacitor lifespan, cost, and volume. For the design and selection of DC-link capacitors in a 1kW permanent magnet synchronous motor drive system, a multi-objective optimization design method for capacitor banks is proposed. The study treats the individual capacitance values of parallel capacitors as variables, significantly enhancing the overall performance of the capacitor module through precise optimization and the introduction of necessary constraints in the selection design. The proposed method is validated with a 1kW permanent magnet synchronous motor drive system. The optimization results show that under the guidance of a multi-objective optimization framework, the designed DC-link capacitor module meets physical size requirements while achieving overall optimization in terms of volume, cost, and lifespan, significantly improving system reliability.

关键词： Solid modeling Costs Capacitors Voltage Permanent magnet motors Capacitance Stability analysis Optimization Thermal stability System analysis and design

Towards Closed-loop Control of the Modified COAST Guidewire under Fluoroscopic Imaging for Endotracheal and Endovascular Interventions

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Towards Closed-loop Control of the Modified COAST Guidewire ...

International Symposium on Medical robotics (ISMR)

作者： Sharan R. Ravigopal Kirsten M. Williams Jaydev P. Desai Department of Biomedical Engineering Medical Robotics and Automation (RoboMed) Laboratory Georgia Institute of Technology Atlanta GA USA Aflac Cancer and Blood Disorders Center Children's Healthcare of Atlanta Emory University Atlanta GA USA

Lung infections in children remain challenging to diagnose despite the use of bronchoalveolar lavage (BAL), in which sterile saline flushes a lung segment and a sample is tested for pathogens. The yield of BAL is low as less fluid can be inserted since the size of the children's lungs is small, and the site of infection is often far distal to the position of the bronchoscope. In addition, it poses additional risks or complications as the saline is also absorbed into the lung parenchyma. A smaller, robotic device could traverse further along the bronchial tree and guide to the site of infection, increasing the successful identification of the pathogen to decrease complications of the BAL procedure. Likewise, treatment of endovascular disease also often involves catheterization, which requires a highly experienced interventional cardiologist to place guidewires at the target location, typically using fluoroscopic imaging to localize the guidewire during the procedure. However, inaccurate placement of these robotic guidewires can lead to unsuccessful procedural outcomes. Robotic manipulation of guidewires could provide better control of the device. We propose an automated approach for closed-loop control of the modified COaxially Aligned STeerable (COAST) guidewire under fluoroscopic imaging in phantoms. Deep learning architectures are used to determine the workspace, position, and orientation of the guidewire in two dimensions. This information is used for path planning and traversal in a phantom. From the centerline of the guidewire, the parameters for the modified COAST guidewire are determined to generate estimated joint actuations, which are compared to the actual joint actuations to autonomously correct for the error in the guidewire tip position for closed-loop control.

关键词： Pathogens Pediatrics Navigation Lung Imaging Phantoms Imaging phantoms

Developing an Integrated Cognitive Test (ICT) for Computerized Assessment of Cognitive Impairment Risk

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Developing an Integrated Cognitive Test (ICT) for Computeriz...

Biomedical engineering International Conference (BMEiCON)

作者： Warissara Limpornchitwilai Chatchai Paengkumhag Wisanu Jutharee Kosin Chamnongthai Boonserm Kaewkamnerdpong Biological Engineering Program Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Field Robotics and Automation King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Electrical Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

In an aging society, dementia is a notable issue that occurs as individuals grow older, impacting cognitive ability and emotional well-being. This has led to the development of cognitive assessment tools to facilitate early detection of cognitive impairment. Limited utilization of computerized tests across different countries is due to the mismatch of existing computerized tests between the educational and cultural backgrounds of the elderly; this study sought to address this issue by developing the Integrated Cognitive Test (ICT). ICT is designed as a computerized tool for assessing the risk of cognitive impairment in Thai older adults with an easy-to-use interface. Thirty-six elderly participants with diverse education levels and without prior cognitive impairment diagnoses participated in the study. They completed MoCA, MMSE, and ICT in a single-day experiment. The scores, reaction time, and overall completion time were recorded for analysis. The study found significant differences between ICT and MMSE scores (p<0.001) but not with MoCA (p=0.421); the results indicated ICT's potential alignment with MoCA for cognitive assessment. The decision tree attained 80.60% accuracy in cognitive impairment risk classification using MoCA output. Key features impacting classification included average reaction time in working memory, memory recall, and standard deviation in language tasks. Overall, the analysis demonstrated that ICT is an effective tool for assessing cognitive impairment, for older adults. Furthermore, designing a cognitive test that considers the unique education and cultural backgrounds of the Thai elderly can serve as a model for creating similar tests for other populations.

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Orbital cluster-based network modelling

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arXiv 2024年

作者： Colanera, Antonio Deng, Nan Chiatto, Matteo de Luca, Luigi Noack, Bernd R. Department of Industrial Engineering University of Naples Federico II Naples80125 Italy Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin10129 Italy Chair of Artificial Intelligence and Aerodynamics School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518055 China

We propose a novel reduced-order methodology to describe complex multi-frequency fluid dynamics from time-resolved snapshot data. Starting point is the Cluster-based Network Model (CNM) thanks to its fully automatable development and human interpretability. Our key innovation is to model the transitions from cluster to cluster much more accurately by replacing snapshot states with short-term trajectories ("orbits") over multiple clusters, thus avoiding nonphysical intra-cluster diffusion in the dynamic reconstruction. The proposed orbital CNM (oCNM) employs functional clustering to coarse-grain the short-term trajectories. Specifically, different filtering techniques, resulting in different temporal basis expansions, demonstrate the versatility and capability of the oCNM to adapt to diverse flow phenomena. The oCNM is illustrated on the Stuart-Landau oscillator and its post-transient solution with time-varying parameters to test its ability to capture the amplitude selection mechanism and multi-frequency behaviours. Then, the oCNM is applied to the fluidic pinball across varying flow regimes at different Reynolds numbers, including the periodic, quasi-periodic, and chaotic dynamics. This orbital-focused perspective enhances the understanding of complex temporal behaviours by incorporating high-frequency behaviour into the kinematics of short-time trajectories while modelling the dynamics of the lower frequencies. In analogy to Spectral Proper Orthogonal Decomposition, which marked the transition from spatial-only modes to spatio-temporal ones, this work advances from analysing temporal local states to examining piecewise short-term trajectories, or orbits. By merging advanced analytical methods, such as the functional representation of short-time trajectories with CNM, this study paves the way for new approaches to dissect the complex dynamics characterising turbulent systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Wakes

Towards synthesizing grasps for 3D deformable objects with physics-based simulation

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arXiv 2021年

作者： Nguyen Le, Tran Lundell, Jens J.Abu-Dakka, Fares Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland

Rebalanced Region-Pixel Loss for Concrete Defects Segmentation 4

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Rebalanced Region-Pixel Loss for Concrete Defects Segmentati...

4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Hong, Kunlong Wang, Hongguang Yang, Liang Yuan, Bingbing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China The CCNY Robotics Lab Electrical Engineering Department City College of New York New York United States

ISBN: (纸本)9781450384087

The deep convolution neural network (DCNN) using cross-entropy loss has already achieved high accuracy on the single defect classification task. However, for multi-defect segmentation, the imbalance of categories makes the segmentation of small targets (cracks) not meticulous enough. It also has to adjust the weight of categories manually. This research proposes a region-pixel loss function, which uses a rebalanced training method to balance category weights automatically and classify small categories more accurately. First, we use a wall-climbing robot to obtain the color and depth (RGB-D) information of the surface. Then, we adopt the visual based simultaneous localization and mapping (V-SLAM) method to select key-frame as the input of DCNN. Finally, reconstructing the 3D model of the environment with the output segmentation results. At the same time, to address the weak links of human-computer interaction in the post-processing of defect monitoring, a fast searching method from global 3D model to local details is proposed so that the staff can calmly obtain the local defects' detailed image. Experiments show that our loss function improves the intersection over union (IoU) of small flaws on the verification set. The designed global-to-local interaction method has been applied in the actual dam surface detection and provided an automated solution for monitoring large concrete surfaces. © 2021 ACM.

关键词： Semantic Segmentation

Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy

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arXiv 2023年

作者： Sun, Hui Luo, Hao Wang, Feifei Chen, Qingjiu Chen, Meng Wang, Xiaoduo Yu, Haibo Zhang, Guanglie Liu, Lianqing Wang, Jianping Wu, Dapeng Li, Wen Jung Department of Mechanical Engineering City University of Hong Kong Hong Kong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Department of Electrical and Electronics Engineering The University of Hong Kong Hong Kong Shenzhen518000 China Department of Computer Science City University of Hong Kong Hong Kong

Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum environment. We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and SEM domain images, which enables the transformation of OSR images into SEM-like large depth-of-field images. Our custom-built scanning superlens microscopy (SSUM) system, which requires neither coating samples by conductive films nor a vacuum environment, is used to acquire the OSR images with features down to ~80 nm. The peak signal-to-noise ratio (PSNR) and structural similarity index measure values indicate that the deep learning method performs excellently in image-to-image translation, with a PSNR improvement of about 0.74 dB over the optical super-resolution images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields. Copyright © 2023, The Authors. All rights reserved.

关键词： Scanning electron microscopy