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2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Voeurn, Yong Ann Raza, Muhammad Ilyas Innopolis University Institute of Robotics and Computer Vision Innopolis Russia

ISBN: (纸本)9781665424066

In motion planning, there are two main emerged directions which are inverse kinematics and control. In this paper, we present the derivation of motion control for a quadruped robot. We proposed inverse kinematics for obstacle avoidance trajectory planning using potential fields. The proposed inverse kinematics is capable of solving a wide range of complex movement skills to quadruped and provides robustness to the singularity configuration. In the control part, we derived LQR control of linearized inverse dynamics with constraints. The proposed algorithm is tested in various experiment cases in both fixed and free moving of center mass(CoM). © 2021 IEEE.

关键词： Inverse kinematics

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Deep Learning Based Segmentation of Breast Lesions in DCE-MRI 25th

Deep Learning Based Segmentation of Breast Lesions in DCE-MR...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Khaled, Roa’a Vidal, Joel Martí, Robert Computer Vision and Robotics Institute University of Girona Girona Spain

ISBN: (纸本)9783030687625

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is a popular tool for the diagnosis of breast lesions due to its effectiveness, especially in a high risk population. Accurate lesion segmentation is an important step for subsequent analysis, especially for computer aided diagnosis systems. However, manual breast lesion segmentation of (4D) MRI is time consuming, requires experience, and it is prone to interobserver and intraobserver variability. This work proposes a deep learning (DL) framework for segmenting breast lesions in DCE-MRI using a 3D patch based U-Net architecture. We perform different experiments to analyse the effects of class imbalance, different patch sizes, optimizers and loss functions in a cross-validation fashion using 46 images from a subset of a challenging and publicly available dataset not reported to date, that is the TCGA-BRCA. We also compare the proposed U-Net framework with another state-of-the-art approach used for breast lesion segmentation in DCE-MRI, and report better segmentation accuracy with the proposed framework. The results presented in this work have the potential to become a publicly available benchmark for this task. © 2021, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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A deep learning based robot positioning error compensation

A deep learning based robot positioning error compensation

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2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Sellami, Sami Klimchik, Alexandr Innopolis University Institute of Robotics and Computer Vision Innopolis Russia

ISBN: (纸本)9781665424066

Robot position accuracy plays a very important role in advanced industrial applications, nowadays, most of the industrial robots have excellent repeatability, however, it still always remain some absolute position error that are due to non geometric calibration parameters that are hard to model and identify. The present work studied a method to reduce the absolute position error of robots using conventional identification procedures as well as neural networks. In order to increase the robot accuracy, we propose to first identify determinable error sources (geometric errors and joint deflection errors), then, use deep learning based methods to identify the non-geometric error sources such as link compliance, gear backlash, and others, which are difficult to model correctly and completely. The algorithm is tested on simulation with the UR-10 robot and is able to identify some predefined parameters with a high level of accuracy using only measurements data and deep learning methods. © 2021 IEEE.

关键词： Industrial robots

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Parameter Identification in Mechanical Systems with Energy-Based Regressor: Preliminary Study

Parameter Identification in Mechanical Systems with Energy-B...

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2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Sabirova, Adelia Nedelchev, Simeon Gaponov, Igor Innopolis University Institute of Robotics and Computer Vision Innopolis Russia

ISBN: (纸本)9781665424066

Parameter estimation in physical systems is an important area of modern engineering and robotics. This paper discusses parameter identification in linear, time-invariant mechanical systems based on their energy. The described approach employs system's regressor derived from the energy equation with subsequent least-squares estimation. This method has been evaluated experimentally on a single degree-of-freedom pendulum and demonstrated high accuracy and quick parameter convergence, while not suffering from the drawbacks of conventional dynamics-based identification. © 2021 IEEE.

关键词： Degrees of freedom (mechanics)

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Planar Shape Control of Deformable Linear Objects

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IFAC-PapersOnLine 2022年第10期55卷 2469-2474页

作者： Karam Almaghout Alexandr Klimchik Robotics and Computer Vision Institute Innopolis University Innopolis Russia

Shape control of deformable linear objects (DLOs) is an open challenge, due to the difficulty in predicting the behavior of the DLOs during the manipulation. In this paper, we propose a new approach to achieve the shape control of a DLO grasped by two robotic manipulators on a plane. The proposed approach models the DLO as a set of points. Then it derives the Jacboian matrix that maps the velocity from the robots end-effectors to the DLO points. Moreover, A strategy to maintain the DLO length constraint during the manipulation is developed to avoid excessive stretching. The new algorithm is tested in simulation environment for different desired shapes. The experiments results prove the efficiency and accuracy of the proposed algorithm to place the DLO into the new shape.

关键词： Robotic manipulation Deformable objects Shape control Simulation

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Towards Combating Frequency Simplicity-biased Learning for Domain Generalization 38

Towards Combating Frequency Simplicity-biased Learning for D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Xilin Hu, Jingyu Lin, Qinliang Luo, Cheng Xie, Weicheng Song, Siyang Khan, Muhammad Haris Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency. © 2024 Neural information processing systems foundation. All rights reserved.

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Underwater Acoustic Localization using pose-graph SLAM

Underwater Acoustic Localization using pose-graph SLAM

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OCEANS

作者： Marta Real Pau Vial Narcís Palomeras Marc Carreras Computer Vision and Robotics Research Institute (VICOROB) University of Girona Girona Spain

Current acoustic localization systems for Autonomous Underwater Vehicles (AUV) rely on acoustic beacons at known positions. We present a method that combines rangeonly acoustic measures and odometry data into a factor graph structure, able to compute the AUV localization while estimating the position of the acoustic beacons without having previous information about their position. We designed several missions and carried them out in a real environment to gather data to test our algorithm offline. The output of this work is a pose-graph SLAM algorithm that combines the AUV internal measurements (i.e. DVL, IMU, and depth) with acoustic ranges from static beacons to estimate both the AUV state and the beacon’s position.

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Inertial navigation framework for multimodal underwater Graph SLAM

Inertial navigation framework for multimodal underwater Grap...

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OCEANS

作者： Pau Vial Miguel Castillón Narcís Palomeras Marc Carreras Pere Ridao Computer Vision and Robotics Research Institute (VICOROB) University of Girona Girona Spain

Robot localization is a fundamental task in achieving true autonomy for Autonomous Underwater Vehicles (AUV). If inertial measurements from an Inertial Measurement Unit (IMU) or a Doppler Velocity Log (DVL) want to be fused with some perception system, such us a multibeam sonar or several acoustic beacons; a full Simultaneous Localization And Mapping (SLAM) problem must be solved. In contrast to filters, in a full SLAM problem the whole robot trajectory is estimated and loop closure events can be detected and closed along it. Common Inertial Navigation Systems (INS), based on filters, only maintain the estimation of the current robot pose. Therefore, these systems cannot be directly used in a full SLAM problem. In this paper we present a graph solution to integrate all inertial measurements in a factor graph that can be extended to different perception modalities and it is solved by applying Smoothing and Mapping (SAM) [1]. The Preintegrated IMU factor, proposed by [2], is combined with priors for other inertial measurements that have been specially designed. This framework is tested on real data from sea experiments, showing how our proposal performance is similar to the estimation provided by high grade commercial INS products based on filters. However, our system has the advantage of allowing for fusion with exteroceptive sensors in SLAM.

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CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis

arXiv

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arXiv 2024年

作者： Alberb, Muhammad Elbatel, Marawan Elgebaly, Aya Montoya-Del-Angel, Ricardo Li, Xiaomeng Martí, Robert Computer Vision and Robotics Institute University of Girona Spain The Hong Kong University of Science and Technology Hong Kong

Digital Breast Tomosynthesis (DBT) is an advanced breast imaging modality that offers superior lesion detection accuracy compared to conventional mammography, albeit at the trade-off of longer reading time. Accelerating lesion detection from DBT using deep learning is hindered by limited data availability and huge annotation costs. A possible solution to this issue could be to leverage the information provided by a more widely available modality, such as mammography, to enhance DBT lesion detection. In this paper, we present a novel framework, CoMoTo, for improving lesion detection in DBT. Our framework leverages unpaired mammography data to enhance the training of a DBT model, improving practicality by eliminating the need for mammography during inference. Specifically, we propose two novel components, Lesion-specific Knowledge Distillation (LsKD) and Intra-modal Point Alignment (ImPA). LsKD selectively distills lesion features from a mammography teacher model to a DBT student model, disregarding background features. ImPA further enriches LsKD by ensuring the alignment of lesion features within the teacher before distilling knowledge to the student. Our comprehensive evaluation shows that CoMoTo is superior to traditional pretraining and image-level KD, improving performance by 7% Mean Sensitivity under low-data setting. Our code is available at https://***/Muhammad-Al-Barbary/CoMoTo. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Lesion Detection in Digital Breast Tomosynthesis: Method, experiences and results of participating to the DBTex Challenge 16

Lesion Detection in Digital Breast Tomosynthesis: Method, ex...

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16th International Workshop on Breast Imaging, IWBI 2022

作者： Martí, Robert Del Campo, Pablo G. Vidal, Joel Cufí, Xavier Martí, Joan Chevalier, Margarita Freixenet, Jordi Computer Vision and Robotics Institute University of Girona Girona Spain Department of Radiology Rehabilitation and Fisiotherapy Universidad Complutense de Madrid Madrid Spain

ISBN: (纸本)9781510655843

The paper presents a framework for the detection of mass-like lesions in 3D digital breast tomosynthesis. It consists of several steps, including pre and post-processing, and a main detection block based on a Faster RCNN deep learning network. In addition to the framework, the paper describes different training steps to achieve better performance, including transfer learning using both mammographic and DBT data. The presented approach obtained third place in the recent DBT Lesion detection Challenge, DBTex, being the top approach without using an ensemble based method. © 2022 SPIE.

关键词： Deep learning

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