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Robot-To-robot relative pose estimation using humans as markers

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arXiv 2019年

作者： Islam, Md Jahidul Mo, Jiawei Sattar, Junaed Interactive Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Twin Cities United States

In this paper, we propose a method to determine the 3D relative pose of pairs of communicating robots by using human pose-based key-points as correspondences. We adopt a -leader-follower' framework, where at first, the leader robot visually detects and triangulates the key-points using the state-of-The-Art pose detector named OpenPose. Afterward, the follower robots match the corresponding 2D projections on their respective calibrated cameras and find their relative poses by solving the perspective-n-point (PnP) problem. In the proposed method, we design an efficient person re-identification technique for associating the mutually visible humans in the scene. Additionally, we present an iterative optimization algorithm to refine the associated key-points based on their local structural properties in the image space. We demonstrate that these refinement processes are essential to establish accurate key-point correspondences across viewpoints. Furthermore, we evaluate the performance of the proposed relative pose estimation method through several experiments conducted in terrestrial and underwater environments. Finally, we discuss the relevant operational challenges of this approach and analyze its feasibility for multi-robot cooperative systems in humandominated social settings and feature-deprived environments such as underwater. Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

Variable Stiffness Control with Strict Frequency Domain Constraints for Physical Human-Robot Interaction

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arXiv 2020年

作者： Zou, Wulin Duan, Pu Chen, Yawen Yu, Ningbo Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Xeno Dynamics Co. Ltd Shenzhen China Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency domain analysis

Fast underwater image enhancement for improved visual perception

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arXiv 2019年

作者： Islam, Md Jahidul Xia, Youya Sattar, Junaed Interactive Robotics and Vision Laboratory Department of Computer Science and Engineering Minnesota Robotics Institute University of Minnesota Twin CitiesMN United States

In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a largescale dataset of a paired and an unpaired collection of underwater images (of 'poor' and 'good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://***/xahidbuffon/ funie-gan. Copyright © 2019, The Authors. All rights reserved.

关键词： Pipelines

Regularization with multilevel non-stationary tight framelets for image restoration

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arXiv 2021年

作者： Li, Yan-Ran Chan, Raymond H.F. Shen, Lixin Zhuang, Xiaosheng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong Department of Mathematics Syracuse University SyracuseNY13244 United States

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

Domain-invariant similarity activation map contrastive learning for retrieval-based long-term visual localization

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arXiv 2020年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Chen, Weidong Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Robotics and System HIT The Department of Computer Science and Technology University of Cambridge United Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified on RobotCar dataset using models pre-trained on urban part of CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under the challenging environments with illumination variance, vegetation and night-time images. The code and pretrained models are available on https://***/HanjiangHu/DISAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

Passive Quadrupedal Gait Synchronization for Extra Robotic Legs Using a Dynamically Coupled Double Rimless Wheel Model

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Passive Quadrupedal Gait Synchronization for Extra Robotic L...

IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel J. Gonzalez H. Harry Asada Robotics Research Center in the Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA d’Arbeloff Laboratory for Information Systems and Technology in the Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human's limbs to coordinate quadrupedal locomotion. Using a pair of Rimless Wheel models, it is shown that the systems coupled with a spring and damper converge to the desired 25% phase difference. A Poincaré return map was generated using numerical simulation to examine the convergence properties to different coupler design parameters, and initial conditions. The Dynamically Coupled Double Rimless Wheel system was physically realized with a spring and dashpot chosen from the theoretical results, and initial experiments indicate that the desired synchronization properties may be achieved within several steps using this set of passive components alone.

关键词： Legged locomotion Wheels Synchronization Couplers Robot kinematics

Soft yet Effective Robots via Holistic Co-Design

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arXiv 2025年

作者： Stölzle, Maximilian Pagliarani, Niccolò Stella, Francesco Hughes, Josie Laschi, Cecilia Rus, Daniela Cianchetti, Matteo Santina, Cosimo Della Zardini, Gioele Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Cognitive Robotics Delft University of Technology Mekelweg 2 CD Delft2628 Netherlands The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Embodied AI AG Lausanne1015 Switzerland CREATE Lab EPFL Lausanne1015 Switzerland Advanced Robotics Centre Department of Mechanical Engineering National University of Singapore 117575 Singapore Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Soft robots promise inherent safety via their material compliance for seamless interactions with humans or delicate environments. Yet, their development is challenging because it requires integrating materials, geometry, actuation, and autonomy into complex mechatronic systems. Despite progress, the field struggles to balance task-specific performance with broader factors like durability and manufacturability—a difficulty that we find is compounded by traditional sequential design processes with their lack of feedback loops. In this perspective, we review emerging co-design approaches that simultaneously optimize the body and brain, enabling the discovery of unconventional designs highly tailored to the given tasks. We then identify three key shortcomings that limit the broader adoption of such co-design methods within the soft robotics domain. First, many rely on simulation-based evaluations focusing on a single metric, while real-world designs must satisfy diverse criteria. Second, current methods emphasize computational modeling without ensuring feasible realization, risking sim-to-real performance gaps. Third, high computational demands limit the exploration of the complete design space. Finally, we propose a holistic co-design framework that addresses these challenges by incorporating a broader range of design values, integrating real-world prototyping to refine evaluations, and boosting efficiency through surrogate metrics and model-based control strategies. This holistic framework, by simultaneously optimizing functionality, durability, and manufacturability, has the potential to enhance reliability and foster broader acceptance of soft robotics, transforming human-robot interactions. © 2025, CC BY.

关键词： Intellectual property core

LMS-Net: A learned Mumford-Shah network for binary few-shot medical image segmentation

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Medical Image Analysis 2025年 105卷

作者： Shengdong Zhang Fan Jia Xiang Li Hao Zhang Jun Shi Liyan Ma Shihui Ying Department of Mathematics School of Science Shanghai University Shanghai 200444 China Department of Mathematics and Scientific Computing and Imaging (SCI) Institute University of Utah Salt Lake City 84102 UT USA School of Computer Science and Technology East China Normal University Shanghai 200444 China School of Communication and Information Engineering Shanghai University Shanghai 200444 China School of Computer Engineering and Science Shanghai University Shanghai 200444 China School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai 200444 China Shanghai Institute of Applied Mathematics and Mechanics Shanghai 200072 China School of Mechanics and Engineering Science Shanghai University Shanghai 200072 China

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMS-Net), for the FSS task. Specifically, motivated by the effectiveness of pixel-to-prototype comparison in prototypical FSS methods and the capability of deep priors to model complex spatial structures, we leverage our learned Mumford-Shah model (LMS model) as a mathematical foundation to integrate these insights into a unified framework. By reformulating the LMS model into prototype update and mask update tasks, we propose an alternating optimization algorithm to solve it efficiently. Further, the iterative steps of this algorithm are unfolded into corresponding network modules, resulting in LMS-Net with clear interpretability. Comprehensive experiments on three publicly available medical segmentation datasets verify the effectiveness of our method, demonstrating superior accuracy and robustness in handling complex structures and adapting to challenging segmentation scenarios. These results highlight the potential of LMS-Net to advance FSS in medical imaging applications. Our code will be available at: https://***/SDZhang01/LMSNet .

关键词： Deep denoising prior Deep unfolding network Few-shot semantic segmentation Mumford-Shah model