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arXiv 2024年

作者： Deng, Tianchen Wang, Nailin Wang, Chongdi Yuan, Shenghai Wang, Jingchuan Wang, Danwei Chen, Weidong The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: (a) inaccurate depth input. Accurate depth input is impossible to get in real-world large-scale scenes. (b) inaccurate pose estimation. Existing methods are not robust enough with the growth of cumulative errors in large scenes and long sequence. (c) insufficient scene representation capability. A single global radiance field lacks the capacity to effectively scale to large-scale scenes. To this end, we propose an incremental joint learning framework, which can achieve accurate depth, pose estimation, and large-scale scene reconstruction. A vision transformer-based network is adopted as the backbone to enhance performance in scale information estimation. For pose estimation, a feature-metric bundle adjustment (FBA) method is designed for accurate and robust camera tracking in large-scale scenes. In terms of implicit scene representation, we propose an incremental scene representation method to construct the entire large-scale scene as multiple local radiance fields to enhance the scalability of 3D scene representation. Extended experiments have been conducted to demonstrate the effectiveness and accuracy of our method in depth estimation, pose estimation, and large-scale scene reconstruction. The code will be open-sourced upon paper acceptance. © 2024, CC BY-NC-SA.

关键词： Depth perception

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The Emerging Intelligent Vehicles and Intelligent Vehicle Carriers Collaborative Systems

The Emerging Intelligent Vehicles and Intelligent Vehicle Ca...

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IEEE Symposium on Intelligent Vehicle

作者： Zhipeng Shen Chao Huang Hailong Huang Yutong Wang Fei-Yue Wang Abbas Jamalipour Duc Truong Pham Ljubo Vlacic Andrey V. Savkin Department of Aeronautical and Aviation Engineering the Hong Kong Polytechnic University Hong Kong Department of Industrial and Systems Engineering the Hong Kong Polytechnic University Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China School of Electrical and Information Engineering University of Sydney Sydney Australia Department of Mechanical Engineering University of Birmingham Birmingham UK Institute of Intelligent & Integrated Systems and the School of Engineering and Built Environment Griffith University Brisbane Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

关键词： Intelligent vehicles Collaboration Charging stations Boosting

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Image Classification with Small Datasets: Overview and Benchmark

arXiv

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arXiv 2022年

作者： Brigato, Lorenzo Barz, Björn Iocchi, Luca Denzler, Joachim Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Mathematics and Computer Science Friedrich Schiller University Jena Germany

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier. © 2022, CC BY.

关键词： Image classification

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NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Yanbo Xie, Hongle Wang, Hesheng Wang, Jingchuan Wang, Danwei Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis. © 2024, CC BY-NC-SA.

关键词： Geometry

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Proximal ADMM for Nonconvex and Nonsmooth Optimization

arXiv

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arXiv 2022年

作者： Yang, Yu Jia, Qing-Shan Xu, Zhanbo Guan, Xiaohong Spanos, Costas J. School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi China CFINS Department of Automation BNRist Tsinghua University Beijing China Electrical Engineering and Computer Sciences University of California Berkeley United States

By enabling the nodes or agents to solve small-sized subproblems to achieve coordination, distributed algorithms are favored by many networked systems for efficient and scalable computation. While for convex problems, substantial distributed algorithms are available, the results for the more broad nonconvex counterparts are extremely lacking. This paper develops a distributed algorithm for a class of nonconvex and nonsmooth problems featured by i) a nonconvex objective formed by both separate and composite components regarding the decision variables of interconnected agents, ii) local bounded convex constraints, and iii) coupled linear constraints. This problem is directly originated from smart buildings and is also broad in other domains. To provide a distributed algorithm with convergence guarantee, we revise the powerful alternating direction method of multiplier (ADMM) method and proposed a proximal ADMM. Specifically, noting that the main difficulty to establish the convergence for the nonconvex and nonsmooth optimization with ADMM is to assume the boundness of dual updates, we propose to update the dual variables in a discounted manner. This leads to the establishment of a so-called sufficiently decreasing and lower bounded Lyapunov function, which is critical to establish the convergence. We prove that the method converges to some approximate stationary points. We besides showcase the efficacy and performance of the method by a numerical example and the concrete application to multi-zone heating, ventilation, and air-conditioning (HVAC) control in smart buildings. © 2022, CC BY-SA.

关键词： Lyapunov functions

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Human-Aware Navigation in Crowded Environments Using Adaptive Proxemic Area and Group Detection

Human-Aware Navigation in Crowded Environments Using Adaptiv...

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

作者： Carlos Medina-Sánchez Simon Janzon Matteo Zella Jesús Capitán Pedro J. Marrón Faculty of Business Administration and Economics Institute for Computer Science and Business Information Systems (ICB) University of Duisburg-Essen Essen Germany Faculty of Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Department of System Engineering and Automation University of Seville Seville Spain

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

关键词：

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Fault Diagnosis and Prognosis in Partially-Observed Discrete Event Systems with Delayed Observations

Fault Diagnosis and Prognosis in Partially-Observed Discrete...

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IEEE Conference on Decision and control

作者： Jiwei Wang Simone Baldi Wenwu Yu Xiang Yin School of Cyber Science and Engineering Southeast University Nanjing China School of Mathematics Southeast University Nanjing China Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Fault diagnosis and prognosis in discrete event systems are studied in the scenario where the observations are possibly received with delay. To address this scenario, two conditions for diagnosis and prognosis with delayed observations are proposed, where we show that the state-of-the-art notion of prognosability must be revised to avoid conservativeness. Diagnosability and prognosability conditions are then verified by introducing a delay observer and a new verification function. Theoretical analysis indicates the effectiveness of the verification method for fault diagnosis and prognosis in the system.

关键词： Fault diagnosis Observers Delays Discrete-event systems Prognostics and health management

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A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Random Cooperative Learning Strategy 22

A Subordinate Multi-Swarm Particle Swarm Optimization Algori...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Li, Mi Wang, YuanYuan Lu, ShengFu Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781450396110

Multi-swarm particle swarm optimization algorithms have received much attention in recent years due to their effectiveness in maintaining the diversity of population. However, during the evolution process, the algorithm often suffers from the aggregation of particles within the sub-swarm which could make the particles fall into the local optimum. In order to solve this problem, we propose a new multi-swarm particle swarm optimization algorithm: A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Cooperative Learning Strategy Algorithm (SMS-DCLS-PSO). In the SMS-DCLS-PSO algorithm, we use the number of stagnant local optimums in the sub-swarms as the aggregation flag. When the sub-swarms reach the aggregation condition, the dynamic random cooperative learning strategy is adopted to realize the adaptive periodic information exchange between sub-swarms. To further enhance the diversity of the population, the best particle of the main population and the worst particle of the sub-swarms are respectively mutated. When the sub-swarms don't meet the aggregation conditions, the sub-swarms evolve independently which means the particles only exchange the information within their own sub-swarm. The main-swarm evaluates the local optimal position of each sub-swarm and its own local optimal position in each iteration to update the velocity. The results of the comparison with SMS-DCLS-PSO and other PSO algorithms on the latest public test suite demonstrate that SMS-DCLS-PSO has a better performance on optimization. © 2022 ACM.

关键词： Particle swarm optimization (PSO)

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Supply Chain Logistics Marine Simulator

Supply Chain Logistics Marine Simulator

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Automatics and Informatics (ICAI), International Conference

作者： Bozhidar Dyakov Milena Karova Aneta Varbanova Ivaylo Penev Ivan Grigorov Danail Dimitrov Department of Navigation Technical University of Varna Varna Bulgaria Department of Computer Science and Engineering Technical University of Varna Varna Bulgaria Department of Industrial Automation Technical University of Varna Varna Bulgaria Department of Software and Internet Technologies Technical University of Varna Varna Bulgaria

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

The paper presents an implementation of a supply chain logistics marine simulator, which realizes document flow for processing of loads, carried by marine transport. The simulator is intended to be used in the educational process in specialties Navigation and Logistics. The main stages of the logistic process are discussed. The architecture of the software system is explained. The essential parts of the process are presented with screenshots from the system.

关键词： Codes Navigation Supply chains computer architecture Software systems Chatbots Road traffic Encryption Informatics computer security

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Particle Swarm Optimization Algorithm with Mirror-image Inertia Weight 22

Particle Swarm Optimization Algorithm with Mirror-image Iner...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Lu, ShengFu Han, JiXin Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781450396110

So far, no matter what kind of inertia weighting algorithm, its value changes within the positive range (0, 1.2). To further improve the optimization performance of particles, this paper proposes a mirror-image inertia weight (miIW) strategy, which extends the value range of inertia weight from the positive domain (0, 1.2) to the negative domain (-1.2, 0). The CEC2014 function was used to test the optimization performance of different inertia weight algorithms in the PSO algorithm using three strategies of positive inertia weight (PIW), negative inertia weight (NIW), and mirror-image inertia weight (miIW), The results show that the optimization performance of miIW is better than PIW and NIW. To verify the actual optimization ability of the mirror-image inertial weight (miIW), we compared the effects of PSO with miIW, PSO with PIW, and PSO with NIW in the optimization of the classification model parameters. The results show that the PSO with miIW optimization result is the best. These research results show that the miIW strategy increases the flight distance of the particles, which expands the search range of the particles, improves the overall development capabilities of the particles, and increases the search density of the particles. The expansion of the search range and the increase of the search density work together to promote a significant improvement in the optimization performance of the PSO algorithm. © 2022 ACM.

关键词： Mirrors

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