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Pseudo-LiDAR for Visual Odometry

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arXiv 2022年

作者： Deng, Huiying Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wu, Xinrui Miao, Yanzi Wang, Hesheng Advanced Robotics Research Center AI Research Institute School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of 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

As one of the important tasks in the field of robotics and machine vision, LiDAR/visual odometry provides tremendous help for various applications such as navigation, location, etc. In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry mainly rely on the input of continuous images. However, it is very complicated for the odometry network to learn the epipolar geometry information provided by the images. In this paper, the concept of pseudo-LiDAR is introduced into the odometry to solve this problem. The pseudo-LiDAR point cloud back-projects the depth map generated by the image into the 3D point cloud, which changes the way of image representation. Compared with the stereo images, the pseudo-LiDAR point cloud generated by the stereo matching network can get the explicit 3D coordinates. Since the 6 Degrees of Freedom (DoF) pose transformation occurs in 3D space, the 3D structure information provided by the pseudo-LiDAR point cloud is more direct than the image. Compared with sparse LiDAR, the pseudo-LiDAR has a denser point cloud. In order to make full use of the rich point cloud information provided by the pseudo-LiDAR, a projection-aware dense odometry pipeline is adopted. Most previous LiDAR-based algorithms sampled 8192 points from the point cloud as input to the odometry network. The projection-aware dense odometry pipeline takes all the pseudo-LiDAR point clouds generated from the images except for the error points as the input to the network. While making full use of the 3D geometric information in the images, the semantic information in the images is also used in the odometry task. The fusion of 2D-3D is achieved in an image-only based odometry. Experiments on the KITTI dataset prove the effectiveness of our method. To the best of our knowledge, this is the first visual odometry method using pseudo-LiDAR. Copyright © 2022, The Authors. A

关键词： Pipelines

Fusing visuo-tactile perception into kernelized synergies for robust grasping and fine manipulation of non-rigid objects

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arXiv 2021年

作者： Katyara, Sunny Deshpande, Nikhil Ficuciello, Fanny Chen, Fei Siciliano, Bruno Caldwell, Darwin G. Department of Advanced Robotics Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Information Technology and Electrical Engineering PRISMA Lab University of Naples Federico II Naples80125 Italy Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

Handling non-rigid objects using robot hands necessities a framework that does not only incorporate human-level dexterity and cognition but also the multi-sensory information and system dynamics for robust and fine interactions. In this research, our previously developed kernelized synergies framework, inspired from human behaviour on reusing same subspace for grasping and manipulation, is augmented with visuo-tactile perception for autonomous and flexible adaptation to unknown objects. To detect objects and estimate their poses, a simplified visual pipeline using RANSAC algorithm with Euclidean clustering and SVM classifier is exploited. To modulate interaction efforts while grasping and manipulating non-rigid objects, the tactile feedback using T40S shokac chip sensor, generating 3D force information, is incorporated. Moreover, different kernel functions are examined in the kernelized synergies framework, to evaluate its performance and potential against task reproducibility, execution, generalization and synergistic re-usability. Experiments performed with robot arm-hand system validates the capability and usability of upgraded framework on stably grasping and dexterously manipulating the non-rigid objects. © 2021, CC BY.

关键词： Robotic arms

Boosting the Efficiency and Mechanical Stability of Organic Solar Cells Through a Polymer Acceptor by Reducing the Elastic Modulus

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advanced Energy Materials 2025年第0期

作者： Wang, Yan Yu, Han Zhao, Dan Liu, Wei Liu, Baoze Wu, Xin Gao, Danpeng Zhang, Dong Zhang, Shoufeng Sun, Xianglang Zhang, Chunlei Zhao, Chaoyue Fu, Yuchen Song, Wei Gong, Shaokuan Fu, Yuang Kwok, Chung Hang Ge, Ziyi Lu, Xinhui Chen, Xihan Xiao, Shuang Wong, Wai-Yeung Chai, Yu Yan, He Zhu, Zonglong Department of Chemistry City University of Hong Kong Kowloon999077 Hong Kong Department of Chemistry Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials Energy Institute and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom 999077 Hong Kong Hong Kong University of Science and Technology-Shenzhen Research Institute No. 9 Yuexing 1st RD Hi-tech Park Nanshan Guangdong Shenzhen518057 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Physics City University of Hong Kong Kowloon999077 Hong Kong Zhejiang Engineering Research Center for Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo315201 China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Department of Physics The Chinese University of Hong Kong Shatin999077 Hong Kong Center for Advanced Material Diagnostic Technology and College of Engineering Physics Shenzhen Technology University Guangdong Shenzhen518118 China Shenzhen Research Institute City University of Hong Kong Shenzhen518057 China

Organic solar cells (OSCs) are regarded as one of the most promising flexible power sources due to their lightweight and flexible properties, with the improvement of photovoltaic and mechanical performance. To improve the current density and power conversion efficiency (PCE), mPh4F-TS (TS) and PYSe2F-T (PA) are introduced into the binary host, PM6/mPh4F-TT (PM6/TT) as third components. It is demonstrated that the corresponding ternary devices, in both rigid and flexible devices, achieved superior efficiencies (19.6%/17.7% for PM6/TT+TS, and 19.2%/17.4% for PM6/TT+PA) outperform the binary counterparts (18.3%/16.4%). However, distinct differences in mechanical performance are observed between the polymer acceptor (PA) and small-molecular acceptor (TS). The PM6/TT+PA significantly improved the mechanical stability of flexible devices with a lower elastic modulus of 3.6 GPa, while the PM6/TT+TS resulted in the opposite effect with a higher elastic modulus of 5.5 GPa. Through in-depth investigation, a clear correlation between the elastic modulus, crack density, and mechanical stability of the active layer blends is successfully established, revealing the key role of reducing the elastic modulus in enhancing the mechanical stability of flexible OSCs. This study provides important guidance for the development of flexible photovoltaic devices with both high efficiency and mechanical robustness. © 2025 The Author(s). advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Organic solar cells

Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance

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arXiv 2022年

作者： Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of 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 Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China

Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point’s movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate scene flow with ground truth for real-world data, this leaves no real-world dataset available to provide a large amount of data with ground truth for scene flow estimation. Therefore, many works use synthesized data to pre-train their network and real-world LiDAR data to finetune. Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network. Supervised odometry provides more accurate shared cost volume for scene flow. In addition, the proposed network has mask-weighted warp layers to get a more accurate predicted point cloud. The warp operation means applying an estimated pose transformation or scene flow to a source point cloud to obtain a predicted point cloud and is the key to refining scene flow from coarse to fine. When performing warp operations, the points in different states use different weights for the pose transformation and scene flow transformation. We classify the states of points as static, dynamic, and occluded, where the static masks are used to divide static and dynamic points, and the occlusion masks are used to divide occluded points. The mask-weighted warp layer indicates that static masks and occlusion masks are used as weights when performing warp operations. Our designs are proved to be effective in ablation experiments. The experiment results show the promising prospect of an odometry-assisted unsupervised learning method for 3D scene flow in real-world data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation 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

3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3D scene flow by estimating optical flow and disparity separately. Learning 3D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this paper, the generated dense depth map is utilized to obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow from 2D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2D pixels into the 3D space. Outliers in the generated 3D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3D scene flow. The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation. © 2022, CC BY.

关键词： Three dimensional computer graphics

Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Myoelectric Prosthetic Hand with Adjustable Tightening Socket for Varied Residual Limb Shapes*

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Myoelectric Prosthetic Hand with Adjustable Tightening Socke...

IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Yuki Inoue Yuki Kuroda Yoshiko Yabuki Yusuke Yamanoi Xu Yong Xiaobei Jing Hiroshi Yokoi Department of Mechanical and Intelligent Engineering The University of Electro-Communications Chofu Tokyo JAPAN OMRON SINIC X Corporation Bunkyo-ku Tokyo JAPAN department of Electrical Engineering Faculty of Engineering Tokyo University of Science Katushika-ku Tokyo JAPAN CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) and the SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen CHINA Department of Mechanical and Intelligent Engineering Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Chofu Tokyo JAPAN Mu-BORG K.K. Chofu JAPAN

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

The prosthetic socket serves as an interface between the body and the machine. If the socket is not well-fitted to the residual limb, pain can occur. The shape of the residual limb changes over time as surgical scars stabilize or the patient’s body weight fluctuates. A socket with a cutout on the side can be tightened from the outside to adjust its circumference and accommodate these changes. However, when the circumference of the rigid socket is adjusted to fit, soft skin can become trapped due to friction between the skin and the socket. This phenomenon causes pain due to pinching. Consequently, this mechanism cannot be used for myoelectric prosthetic hands, where direct contact between the socket and the skin is necessary. In this study, we developed a socket with a variable circumference that suppresses skin pinching. We applied it to participants with different residual limb shapes. A Box and Block Test (BBT) was performed using two types of sockets. The first used a conventional socket designed to fit their residual limb shape. The second used the proposed socket, which was designed to fit the residual limb’s shape of one out of two participants and adapted to the residual limb’s changing shape. The results of the BBT showed not much difference in the number of blocks moved between the conventional socket and the proposed socket. Therefore, the performance of the proposed socket was comparable to that of conventional sockets and could be applied to different residual limbs. In other words, the proposed socket was shown to adapt to changes in the residual limb without causing pain. Sockets with the proposed functionality could be used in a variety of bio-interfaces, allowing for flexible connections between the body and machines.

关键词： Limbs Shape Pain Sockets Human-machine systems Friction Surgery Skin Interviews Prosthetic hand

Optomechanical dissipative solitons (vol 600, pg 75, 2021)

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NATURE 2022年第7896期602卷 E24-E24页

作者： Zhang, Jing Peng, Bo Kim, Seunghwi Monifi, Faraz Jiang, Xuefeng Li, Yihang Yu, Peng Liu, Lianqing Liu, Yu-xi Alu, Andrea Yang, Lan Department of Electrical and Systems Engineering Washington University St. Louis MO USA Department of Automation Tsinghua University Beijing P. R. China Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Physics Program Graduate Center City University of New York New York NY USA State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang P. R. China Institute of Microelectronics Tsinghua University Beijing P. R. China

Study of Oxygen Reduction Reaction on Bi-Nuclear-Phthalocyanine with Fe-Fe, Co-Co, and Fe-Co Dual-Atom-Active Sites Using Density Functional Theory

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SSRN

SSRN 2022年

作者： Kumar, Anuj Ibraheem, Shumaila Das, Dipak Kumar Sharma, Raj Kishore Ajmal, Saira Nadda, Ashok Kumar Zhang, Guoxin Gupta, Ram K. Yasin, Ghulam Nano-Technology Research Laboratory Department of Chemistry GLA University Uttar Pradesh Mathura281406 India Institute for Advanced Study Shenzhen University Guangdong Shenzhen518060 China Department of Chemistry University of Delhi Delhi110007 India Department of Chemistry Kansas Polymer Research Center Pittsburg State University PittsburgKS66762 United States Department of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China Department of Biotechnology and Bioinformatics Jaypee University of Information Technology Solan Waknaghat173 234 India

Although the N4-macrocyclic ligands have been used to develop single-atom catalysts, their utilization for the construction of dual-atom catalysts for oxygen reduction reactions is poorly investigated. Herein, a bi-nuclear phthalocyanine (bN-Pc) was explored as a theoretical model for the construction of FeFe-bN-Pc, CoCo-bN-Pc, and FeCo-bN-Pc dual-atom-sites configurations and their ORR activity and mechanisms were investigated systematically in alkaline media, using density functional theory. The results indicated that the dual-atom-bN-Pc models, having close proximity between adjacent metals, invited O2 for the mutual coordination of each O-atom on dual-atom sites, forming a cis-bridged-O2 adduct. The Gibbs free energy studies showed that the decomposition of O2 on dual-atom sites is the rate-determining step, and the FeCo bN-Pc had a lower limiting potential of 0.553 eV for this step as compared to Fe-Fe-bN-Pc (0.641 eV), and Co-Co-bN-Pc (0.687 eV), which indicates its stronger ORR activity. The synergistic effect of Fe-Co inside collaboration, the close proximity of Fe-Co, and significant electron donation from the 3d-orbital of active sites into the *-orbital of O2 can be attributed to this decrease in limiting the potential for the rate-determining step on Fe-Co-bN-Pc. For future ORR electrocatalysts, this work presents a scientific and engineering perspective on the construction of dual-atom active sites employing molecular moieties. © 2022, The Authors. All rights reserved.

关键词： Density functional theory