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Efficient Planar Pose Estimation via UWB Measurements

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arXiv 2022年

作者： Jiang, Haodong Wang, Wentao Shen, Yuan Li, Xinghan Ren, Xiaoqiang Mu, Biqiang Wu, Junfeng China School of Data Science The Chinese University of HongKong Shenzhen China College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

state estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation. Copyright © 2022, The Authors. All rights reserved.

关键词： state estimation

NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations

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arXiv 2022年

作者： Ran, Yunlong Zeng, Jing He, Shibo Chen, Jiming Li, Lincheng Chen, Yingfeng Lee, Gimhee Ye, Qi The College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Fuxi AI Lab NetEase Inc. Hangzhou China The Department of Computer Science National University of Singapore Singapore

Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality;secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning. Project webpage https://***/NeurAR/. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

Smart Contract Service Optimization in Blockchain-Cloud Collaborative Computing

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Smart Contract Service Optimization in Blockchain-Cloud Coll...

International Conference on Mobile Data Management

作者： Ji Wan Kai Hu Jie Li Hao Su Qingshun Wu Mingyuan Li Libo Feng Yan Pan State Key Laboratory of Software Development Environment Beihang University Beijing China Yunnan Key Laboratory of Blockchain Application Technology Beihang Yunnan Innovation Institute Kunming China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China The First Affiliated Hospital of Zhengzhou University Zhengzhou China National Pilot School of Software Yunnan University Kunming China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain.

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Efficient Planar Pose Estimation via UWB Measurements

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Efficient Planar Pose Estimation via UWB Measurements

IEEE International Conference on Robotics and Automation (ICRA)

作者： Haodong Jiang Wentao Wang Yuan Shen Xinghan Li Xiaoqiang Ren Biqiang Mu Junfeng Wu Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) School of Data Science the Chinese University of HongKong Shenzhen P. R. China College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou P. R. China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing P. R. China School of Mechatronic Engineering and Automation Shanghai University Shanghai P. R. China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China

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HILONet: Hierarchical imitation learning from non-aligned observations

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arXiv 2020年

作者： Liu, Shanqi Cao, Junjie Chen, Wenzhou Wen, Licheng Liu, Yong The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang310027 China

It is challenging learning from demonstrated observation-only trajectories in a non-time-aligned environment because most imitation learning methods aim to imitate experts by following the demonstration step-by-step. However, aligned demonstrations are seldom obtainable in real-world scenarios. In this work, we propose a new imitation learning approach called Hierarchical Imitation Learning from Observation(HILONet), which adopts a hierarchical structure to choose feasible subgoals from demonstrated observations dynamically. Furthermore, our method can learn different kinds of policy corresponding to different tasks by using a novel reward structure. We also present three different ways to increase sample efficiency in the hierarchical structure. We conduct extensive experiments using several environments. The results show the improvement in both performance and learning efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Efficiency

Search-based kinodynamic motion planning for omnidirectional quadruped robots

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arXiv 2020年

作者： Wang, Pei Zhou, Xiaoyu Zhao, Qingteng Wu, Jun Zhu, Qiuguo The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang310027 China

Autonomous navigation has played an increasingly significant role in quadruped robot system. However, most existing works on quadruped robots navigation using traditional search-based or sample-based methods do not consider the kinodynamic characteristics of quadruped robots, generating kinodynamically infeasible parts, that are difficult to track. In this paper, we introduce a complete navigation system considering the omnidirectional abilities of quadruped robots. First, we use kinodynamic path finding method to obtain smooth, dynamically feasible, time-optimal initial paths and add collision cost as a soft constraint to ensure safety. Then the trajectory is refined by the timed elastic band (TEB) method based on the omnidirectional model of quadruped robots. The superior performance of our work is demonstrated through simulating and real-world experiments on our quadruped robot Jueying Mini. Copyright © 2020, The Authors. All rights reserved.

关键词： Multipurpose robots

Alternating minimization based trajectory generation for quadrotor aggressive flight

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arXiv 2020年

作者： Wang, Zhepei Zhou, Xin Xu, Chao Chu, Jian Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

With much research has been conducted into trajectory planning for quadrotors, planning with spatial and temporal optimal trajectories in real-time is still challenging. In this paper, we propose a framework for generating large-scale piecewise polynomial trajectories for aggressive autonomous flights, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the planning problem, we conduct alternating minimization between boundary conditions and time durations of trajectory pieces. In each minimization phase, we leverage the algebraic convenience of the sub-problem to escape poor local minima and achieve the lowest time consumption. Theoretical analysis for the global/local convergence rate of our proposed method is provided. Moreover, based on polynomial theory, an extremely fast feasibility check method is designed for various kinds of constraints. By incorporating the method into our alternating structure, a constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art methods regarding efficiency, optimality, and scalability. Aggressive flight experiments in a limited space with dense obstacles are presented to demonstrate the performance of the proposed algorithm. We release our implementation as an open-source ros-package1. Copyright © 2020, The Authors. All rights reserved.

关键词： Constrained optimization

Moving forward in formation: A decentralized hierarchical learning approach to multi-agent moving together

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arXiv 2020年

作者： Liu, Shanqi Wen, Licheng Cui, Jinhao Yang, Xuemeng Cao, Junjie Liu, Yong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang310027 China

Multi-agent path finding in formation has many potential real-world applications like mobile warehouse robots. However, previous multi-agent path finding (MAPF) methods hardly take formation into consideration. Furthermore, they are usually centralized planners and require the whole state of the environment. Other decentralized partially observable approaches to MAPF are reinforcement learning (RL) methods. However, these RL methods encounter difficulties when learning path finding and formation problem at the same time. In this paper, we propose a novel decentralized partially observable RL algorithm that uses a hierarchical structure to decompose the multi-objective task into unrelated ones. It also calculates a theoretical weight that makes every task’s reward has equal influence on the final RL value function. Additionally, we introduce a communication method that helps agents cooperate with each other. Experiments in simulation show that our method outperforms other end-to-end RL methods and our method can naturally scale to large world sizes where centralized planner struggles. We also deploy and validate our method in a real-world scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

Radar-on-Lidar: metric radar localization on prior lidar maps

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arXiv 2020年

作者： Yin, Huan Wang, Yue Tang, Li Xiong, Rong The State Key Laboratory of Industrial Control and Technology The Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes. Copyright © 2020, The Authors. All rights reserved.

关键词： Radar measurement