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IEEE International Conference on robotics and Automation (ICRA)

作者： Guangyang Zeng Shiyu Chen Biqiang Mu Guodong Shi Junfeng Wu School of Data Science Chinese University of Hong Kong Shenzhen P. R. China Key Laboratory of Systems and Control Institute of Systems Science Beijing P. R. China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China The Australian Center for Field Robotics School of Aerospace Sydney Mechanical and Mechatronic Engineering The University of Sydney Sydney Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) Shenzhen P. R. China

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has $O(n)$ time complexity. Simulations and real dataset tests show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time.

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QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

QuerySOD: A Small Object Detection Algorithm Based on Sparse...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks Security Noise measurement

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A toolkit to generate social navigation datasets

arXiv

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arXiv 2020年

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Dept. of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Goa India Robotics and Artificial Vision Laboratory University of Extremadura Spain Dept. of Computer Engineering Pune Institute of Computer Technology India Dept. of Computer Science College of Engineering and Physical Sciences Aston University United Kingdom

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. Copyright © 2020, The Authors. All rights reserved.

关键词： Navigation

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Obstacle detection for a vehicle using optical flow

Obstacle detection for a vehicle using optical flow

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IEEE Symposium on Intelligent Vehicle

作者： G.-S. Young T.-H. Hong M. Herman J.C.S. Yang Robotics Laboratory Department of Mechanical Engineering University of Maryland College park MD USA National Institute for Standards and Technology Gaithersburg MD USA Department of Computer Science and Information Systems American University Washington D.C. DC USA

A novel approach to obstacle detection using optical flow without recovering range information has been developed. This method can be used for ground vehicles to navigate through man-made roadways or natural outdoor terrain or for air vehicles to land on known or unknown terrain. A linear relationship, plotted as a line and called a reference flow line, has been found. This reference flow line can be used to detect discrete obstacles above or below the reference terrain. Slopes of surface regions are also computed. The approach is simple, fast, and robust because: (1) the only required information is one component of the optical flow, (2) each image line can be processed in parallel, and (3) the error sources involved are reduced to a minimum. An initial experiment using noisy synthetic data is also included to demonstrate the applicability and robustness of the method.< >

关键词： Vehicle detection Image motion analysis Optical sensors Biomedical optical imaging Navigation Land vehicles Cameras Road vehicles Robustness Remotely operated vehicles

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Particle swarms with dynamic topologies and conservation of function evaluations 6

Particle swarms with dynamic topologies and conservation of ...

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6th International Conference on Evolutionary Computation Theory and Applications, ECTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014

作者： Fernandes, Carlos M. Laredo, Juan L. J. Merelo, Juan Julian Cotta, Carlos Rosa, Agostinho LARSyS: Laboratory for Robotics and Systems in Engineering and Science University of Lisbon Av. Rovisco Pais 1 Lisbon Portugal Faculty of Sciences Technology and Communications University of Luxembourg 6 rue Richard Coudenhove-Kalergi LuxembourgL-1359 Luxembourg Departamento de Arquitectura y Tecnología de Computadores University of Granada C Daniel Saucedo Aranda s/n Granada18071 Spain Universidad de Málaga Campus de Teatinos Malaga29071 Spain

ISBN: (纸本)9789897580529

This paper proposes a general framework for structuring dynamic Particle Swarm populations and uses a conservation of function evaluations strategy to increase the convergence speed. The population structure is constructed by placing the particles on a 2-dimensional grid of nodes, where they interact and move according to simple rules. During the running time of the algorithm, the von Neumann neighborhood is used to decide which particles influence each other when updating their velocity and position. Each particle is updated in each time-step but they are evaluated only if there are other particles in their neighborhood. A set of experiments demonstrates that the dynamics imposed by the structure provides a more consistent and stable behavior throughout the test set when compared to standard topologies, while the conservation of evaluations significantly reduces the convergence speed of the algorithm. Furthermore, the working mechanisms of the proposed structure are very simple and, except for the size of the grid, they do not require parameters and tuning.

关键词： Swarm intelligence

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Various emotional expressions with emotion expression humanoid robot WE-4RII

Various emotional expressions with emotion expression humano...

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IEEE Technical Exhibition Based Conference on robotics and Automation, TExCRA

作者： K. Itoh H. Miwa M. Matsumoto M. Zecca H. Takanobu S. Roccella M.C. Carrozza P. Dario A. Takanishi Graduate School of Science and Engineering Waseda University Tokyo Japan Department of Mechanical Engineering Waseda University Tokyo Japan ARTS Laboratory Scuola Superiore Sant'' Anna Pontedera Italy Humanoid Robotics Institute Waseda University Tokyo Japan Department of Mechanical Systems Engineering Kogakuin University Tokyo Japan

The authors have been developing humanoid robots in order to develop new mechanisms and functions for a humanoid robot that has the ability to communicate naturally with a human by expressing human-like emotion. In 2004, we developed the emotion expression humanoid robot WE-4RII (Waseda Eye No.4 Refined II) by integrating the new humanoid robot hands RCH-I (RoboCasa Hand No.1) into the emotion expression humanoid robot WE-4R. We confirmed that WE-4RII can effectively express its emotion.

关键词： Humanoid robots Robot kinematics Emotion recognition Subspace constraints Olfactory Robot sensing systems Human robot interaction Motion pictures Mechanical engineering Mechanical systems

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A Novel Methodology for Robust Dynamic Positioning of Marine Vessels: Theory and Experiments

A Novel Methodology for Robust Dynamic Positioning of Marine...

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American Control Conference

作者： Vahid Hassani Asgeir J. Sorensen Antonio M. Pascoal Norwegian Marine Technology Research Institute (MARINTEK) Trondheim Norway Centre for Autonomous Marine Operations and Systems (AMOS) and Dept. of Marine Technology Norwegian Univ. of Science and Technology Trondheim Norway Laboratory of Robotics and Systems in Engineering and Science (LARSyS) Instituto Superior Tecnico (IST) Univ. Tecnica de Lisboa (UTL) Lisbon Portugal

ISBN: (纸本)9781479901777

The paper describes a novel robust adaptive controller for Dynamic Positioning (DP) of marine vessels. The proposed Robust Multiple Model Adaptive Dynamic Positioning (RMMADP) structure consists of a bank of robust controllers designed using the Mixed-μ methodology and an identification unit. The latter is composed by a bank of (steady-state) Kalman filters (KFs) that generate online the output estimation errors (residuals) that are used to generate appropriate monitoring signals. At each sampling time, the monitoring signals are assessed to decide which controller should be selected from the bank of the controllers. The proposed adaptive structure of the RMMADP enables the DP system to operate in different operational conditions and hence, it is a step forward to a so-called all-year marine DP system. Numerical simulations, carried out with a high fidelity nonlinear DP simulator, illustrate the efficacy of the RMMADP techniques proposed. To bridge the gap between theory and practice, the results are experimentally verified by model testing a DP operated ship, the Cybership III, under different sea conditions in a towing tank equipped with a hydraulic wave maker.

关键词： DP RMMADP Cybership III

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A Context-Enhanced Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping

A Context-Enhanced Full-Resolution Floor Plan Segmentation N...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Yiyang Sun Zhe Ma MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Topological semantic maps provide a practical solution to enhance indoor navigation for the Partially Sighted or Visually Impaired (PSVI). Segmenting indoor floor plans and extracting boundaries are key to constructing these maps. The existing methods exhibit low accuracy in segmentation. To achieve desired high segmentation accuracy, we introduce a Context-Enhanced Full-Resolution Network (CEFRN) for floor plan segmentation. It is designed to harness the shallow detailed features and inter-category contextual dependencies inherent in floor plans. CEFRN integrates modified residual blocks to capture the low-stage full-resolution features while maintaining its compactness. A position attention module is employed to refine the deep-stage contextual information. We also propose a two-dimensional deep supervision method to merge features from both stages, which significantly boosts the feature representation ability of CEFRN. Finally, a practical topological semantic mapping method for PSVI indoor navigation is introduced. Experimental results demonstrate that CEFRN’s segmentation accuracy well exceeds the state-of-the-art methods’. It can be used to well support accurate topological semantic mapping.

关键词： Accuracy Hospitals Indoor navigation Semantics Feature extraction Mobile robots Floors Intelligent robots Autonomous vehicles Drones

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BLIP-Adapter: Bridging Vision-Language Models with Adapters for Generalizable Face Anti-spoofing 18

BLIP-Adapter: Bridging Vision-Language Models with Adapters ...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Bin Zhu, Xiangyu Gao, Li Zhang, Xiaoyu Lei, Zhen Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China China Mobile Financial Technology Co. Ltd. China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Hong Kong

ISBN: (纸本)9798350364132

Face anti-spoofing is essential for ensuring the security of facial recognition systems against spoofing attacks. Recent methods have transferred Vision-Language models to face anti-spoofing (e.g., FLIP and CLIPC8), demonstrating that learning perception from supervision in natural language can enhance the model's detection performance. However, such methods exhibit limited depth in the interaction between images and texts, resulting in poor performance on fine-grained understanding tasks such as face anti-spoofing. Besides, the lack of diversity in image-text pairs for face anti-spoofing further hinders such methods from playing their best. To address these issues, we propose a novel fine-tuning strategy for Vision-Language models in face anti-spoofing. This strategy introduces the Bootstrapping Language-Image Pre-training model (BLIP), known for its novel interaction mechanisms and superior image-text comprehension, to construct a more generalized feature representation for face anti-spoofing. Furthermore, we propose an Adapter module for the text branch to reduce the negative impact of insufficient data diversity and catastrophic forgetting. Extensive experiments conducted on various cross-domain testing benchmarks demonstrate the significant superiority of our method over the state-of-the-art, highlighting its effectiveness and robustness. © 2024 IEEE.

关键词： Visual languages

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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