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DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition

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arXiv 2022年

作者： Lu, Sha Xu, Xuecheng Tang, Li Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control and Technology The Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China Alibaba Group Hangzhou310052 China

LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with large perspective difference. To address the challenge, we propose DeepRING to learn the roto-translation invariant representation from LiDAR scan, so that robot visits the same place with different perspective can have similar representations. There are two keys in DeepRING: the feature is extracted from sinogram, and the feature is aggregated by magnitude spectrum. The two steps keeps the final representation with both discrimination and roto-translation invariance. Moreover, we state the place recognition as a one-shot learning problem with each place being a class, leveraging relation learning to build representation similarity. Substantial experiments are carried out on public datasets, validating the effectiveness of each proposed component, and showing that DeepRING outperforms the comparative methods, especially in dataset level generalization. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

LiDAR-inertial 3D SLAM with plane constraint for multi-story building

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arXiv 2022年

作者： Zhang, Jiashi Zhang, Chengyang Wu, Jun Jin, Jianxiang Zhu, Qiuguo State Key Laboratory Of Industrial Control And Technology Zhejiang University Institute Of Cyber-System And Control Zhejiang University Hangzhou China

The ubiquitous planes and structural consistency are the most apparent features of indoor multi-story Buildings compared with outdoor environments. In this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with plane features for the multi-story building. The framework we proposed is mainly composed of three parts: tightly coupled LiDAR-Inertial odometry, extraction of representative planes of the structure, and factor graph optimization. By building a local map and inertial measurement unit (IMU) pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements, respectively. Minimize the joint cost function to obtain the LiDAR-Inertial odometry information. Once a new keyframe is added to the graph, all the planes of this keyframe that can represent structural features are extracted to find the constraint between different poses and stories. A keyframe-based factor graph is conducted with the constraint of planes, and LiDAR-Inertial odometry for keyframe poses refinement. The experimental results show that our algorithm has outstanding performance in accuracy compared with the state-of-the-art algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost functions

Real-time Whole-body Motion Planning for Mobile Manipulators Using Environment-adaptive Search and Spatial-temporal Optimization

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Real-time Whole-body Motion Planning for Mobile Manipulators...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Chengkai Wu Ruilin Wang Mianzhi Song Fei Gao Jie Mei Boyu Zhou School of Artificial Intelligence Sun Yat-Sen University Zhuhai China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China State Key Laboratory of Industrail Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Mobile manipulators have recently gained significant attention in the robotics community due to their superior potential in industrial and service applications. However, the high degree of freedom associated with mobile manipulators poses challenges in achieving real-time whole-body motion planning. To bridge the gap, this paper presents a motion planning method capable of generating high-quality, safe, agile and feasible trajectories for mobile manipulators in real time. First, we present a novel environment-adaptive path searching method, which can generate paths in real-time in various environments by adaptively adjusting searching dimension based on environment complexity. Additionally, we propose a real-time spatial-temporal trajectory optimization method that takes into account the whole-body safety, agility and dynamic feasibility of mobile manipulators. Moreover, task constraints are applied to ensure that the trajectory can fulfill specific task requirements. Simulation and real-world experiments demonstrate that our method is capable of generating whole-body trajectories in real-time in challenging environments. We will release our code to benefit the community.

关键词： Service robots Dynamics Transportation Real-time systems Planning Safety Complexity theory

GOOD: General Optimization-based Fusion for 3D Object Detection via LiDAR-Camera Object Candidates

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arXiv 2023年

作者： Shen, Bingqi Dai, Shuwei Chen, Yuyin Xiong, Rong Wang, Yue Jiao, Yanmei The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Hangzhou Iplus Technology Co Ltd Hangzhou China The School of Information Science and Engineering Hangzhou Normal University Hangzhou311121 China

3D object detection serves as the core basis of the perception tasks in autonomous driving. Recent years have seen the rapid progress of multi-modal fusion strategies for more robust and accurate 3D object detection. However, current researches for robust fusion are all learning-based frameworks, which demand a large amount of training data and are inconvenient to implement in new scenes. In this paper, we propose GOOD, a general optimization-based fusion framework that can achieve satisfying detection without training additional models and is available for any combinations of 2D and 3D detectors to improve the accuracy and robustness of 3D detection. First we apply the mutual-sided nearest-neighbor probability model to achieve the 3D-2D data association. Then we design an optimization pipeline that can optimize different kinds of instances separately based on the matching result. Apart from this, the 3D MOT method is also introduced to enhance the performance aided by previous frames. To the best of our knowledge, this is the first optimization-based late fusion framework for multi-modal 3D object detection which can be served as a baseline for subsequent research. Experiments on both nuScenes and KITTI datasets are carried out and the results show that GOOD outperforms by 9.1% on mAP score compared with PointPillars and achieves competitive results with the learning-based late fusion CLOCs. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Smart Contract Service Optimization in Blockchain-Cloud Collaborative Computing 24

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

24th IEEE International Conference on Mobile Data Management, MDM 2023

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

ISBN: (纸本)9798350341010

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. © 2023 IEEE.

关键词： Smart contract

Adaptive Robust Tracking control for First-Order Linear systems with Input Delay and Lipschitz Nonlinear Disturbance

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Adaptive Robust Tracking Control for First-Order Linear Syst...

American control Conference (ACC)

作者： Han Lai Yang Zhu Zheng Chen Bin Yao State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Ocean College Zhejiang University Zhoushan China School of Mechanical Engineering Purdue University West Lafayette IN USA

In this paper, an adaptive robust tracking controller is proposed for first-order linear systems with input delay, unknown plant parameters and Lipschitz nonlinear disturbance. The controller employs the predictor feedback to compensate for the effect of input delay, the robust feedback to deal with uncertainties, the model compensation for trajectory tracking, and projection-type adaptation laws are designed. By the stability analysis with a Lyapunov function in integral form, the closed-loop system is locally stable in the sense that the tracking error is bounded above by a known function which exponentially converges to a specified accuracy provided that the initial states and control parameters meet certain conditions. Furthermore, when the disturbance is reduced to a constant, the controller guarantees the semi-global stability that the tracking error asymptotically converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.

关键词：

Robust and Efficient Trajectory Planning for Formation Flight in Dense Environments

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arXiv 2022年

作者： Quan, Lun Yin, Longji Zhang, Tingrui Wang, Mingyang Wang, Ruilin Zhong, Sheng Zhou, Xin Cao, Yanjun Xu, Chao Gao, Fei The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China The Huzhou Institute Zhejiang University Huzhou313000 China

Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method that adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the global planning and local trajectory optimizations. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

Certifiably Optimal Mutual Localization with Anonymous Bearing Measurements

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arXiv 2022年

作者： Wang, Yingjian Wen, Xiangyong Yin, Longji Xu, Chao Cao, Yanjun Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China

Mutual localization is essential for coordination and cooperation in multi-robot systems. Previous works have tackled this problem by assuming available correspondences between measurements and received odometry estimations, which are difficult to acquire, especially for unified robot teams. Furthermore, most local optimization methods ask for initial guesses and are sensitive to their quality. In this paper, we present a certifiably optimal algorithm that uses only anonymous bearing measurements to formulate a novel mixed-integer quadratically constrained quadratic problem (MIQCQP). Then, we relax the original nonconvex problem into a semidefinite programming (SDP) problem and obtain a certifiably global optimum using with off-the-shelf solvers. As a result, our method can determine bearing-pose correspondences and furthermore recover the initial relative poses between robots under a certain condition. We compare the performance with local optimization methods on extensive simulations under different noise levels to show our advantage in global optimality and robustness. Real-world experiments are conducted to show the practicality and robustness. © 2022, CC BY.

关键词： Multipurpose robots

Simulated Ising Annealing Algorithm with Gaussian Augmented Hamiltonian Monte Carlo

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Simulated Ising Annealing Algorithm with Gaussian Augmented ...

Chinese control Conference (CCC)

作者： Langyu Li Hao Wang Zhixin Xie Zixuan Liu Wei Cui Yu Pan State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China School of Mechanical Engineering and Automation Harbin Institude of Technology Shenzhen China

A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization (QUBO) problem in combinatorial optimization, which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo (GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.

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