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Live Demonstration: A Reconfigurable, Energy-efficient and High-frame-rate EKF-SLAM Accelerator Based SoC Design for Autonomous Mobile Robot Applications

Live Demonstration: A Reconfigurable, Energy-efficient and H...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Dingcheng Jiang Bingqiang Liu Jipeng Wang Ao Hu Yequan Zhao Minjie Bao Zhendong Fan Zixuan Shen Ke Wang Chao Wang School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China School of Future Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This demonstration shows a Extend Kalman Filter-Simultaneous Localization And Mapping (EKF-SLAM) accelerator based system On Chip (SoC) design for Autonomous Mobile Robots (AMR). The AMR platform consists of a multi-sensor system with a wheel encoder and LiDAR, and a ZYNQ-7000 FPGA based SoC featuring an EKF-SLAM hardware accelerator. This AMR system achieves real-time SLAM with significant energy efficient improvement against the state-of-the-art designs.

关键词： Location awareness Simultaneous localization and mapping Laser radar Wheels Energy efficiency Real-time systems system-on-chip

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UniTSFace: unified threshold integrated sample-to-sample loss for face recognition 23

UniTSFace: unified threshold integrated sample-to-sample los...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Qiufu Li Xi Jia Jiancan Zhou Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and School of Computer Science University of Birmingham UK National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and Aqara Lumi United Technology Co. Ltd China School of Computer Science University of Birmingham UK and Alan Turing Institute UK

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace.

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Research on Medical Image Encryption Method Based on Chaotic Scrambling and Compressed Sensing 1st

Research on Medical Image Encryption Method Based on Chaotic...

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1st International Conference on Big Data and Security, ICBDS 2019

作者： Liu, Yubin Pang, Ming State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China College of Automation Harbin Engineering University Harbin China

ISBN: (纸本)9789811575297

In order to improve the security of medical images, a new image encryption method is proposed in this paper. In this method, the chaos theory is used to set the initial state and key, and then the measurement equation constructed by compressed sensing is used to complete the fusion encryption. The fusion of the two theories achieves the effect of chaotic uniform scrambling on the one hand, and on the other hand, the encrypted image can still accurately recover the real image when it is compressed to a fairly small size. Experiments are carried out on three groups of medical images. The experimental results show that the proposed encryption method achieves an ideal encryption effect for medical images. Under the condition that the encrypted image is compressed to 1/4 of the original image, the decryption can still be completed effectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

arXiv

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arXiv 2023年

作者： Li, Shilei Shi, Dawei Lou, Yunjiang Zou, Wulin Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Xeno Dynamics Control Department Xeno Dynamics Co. Ltd Shenzhen518055 China

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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Coordinate Descent Clustering with Trace Difference Criterion for Joint Clustering and Feature Extraction

SSRN

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SSRN 2023年

作者： Wang, Quan Wang, Fei Li, Zhongheng Wang, Zheng Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

Joint clustering and dimensionality reduction methods are a promising solution to clustering due to its scalability to high-dimensional data. Some methods leverage trace ratio criterion and attain clusters by borrowing the K-means algorithm. However, trace ratio criterion has no close-formed solution for the discriminative projection matrix and the K-means algorithm has a limited capacity to handle the many-cluster problem. In this paper, Coordinate Descent Clustering with Trace Difference criterion (CDCTD) is proposed for joint clustering and feature extraction. Formulating the objective function as a direct trace difference criterion containing a balance parameter, CDCTD harmonizes between-cluster scatter maximization and within-cluster scatter minimization by the balance parameter. Using the direct trace difference criterion, CDCTD can straightforward solve for the discriminative projection matrix and avoid obtaining a poor discriminative projection matrix in the iterative manner when a bad cluster start is given. CDCTD uses the coordinate descent method for clustering, improving the ability to address the many-cluster problem. Extensive experiments show that CDCTD has achieved significant performance improvements compared to previous trace ratio criterion related joint clustering and feature extraction methods, and also outperformed other clustering methods in most cases. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Rehabilitation Training of Spinal Cord Injured Individuals by a Self-Balancing Exoskeleton Robot

Rehabilitation Training of Spinal Cord Injured Individuals b...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Wentao Li Jinke Li Yong He Dingkui Tian Feng Li Jianquan Sun Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China China Construction Third Bureau First Engineering MEP Co. Ltd. Shenzhen China

Physical therapy plays an important role in the rehabilitation for spinal cord injured individuals, and exoskeleton can assist with this process. In this paper, a self-balancing exoskeleton robot is introduced, which can provide assist not only in gait training, but also in balance training. In addition, it allows individuals walk with it without other assistive devices. This exoskeleton is designed to have a high degree of compatibility with humans. The structure and control system of the exoskeleton is introduced and the forward and inverse kinematic are solved, which enables the robot to perform various rehabilitation training actions as required. Experiments with a male subject have shown that this exoskeleton achieves the predicted results in gait training and balance training.

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Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks with Tree Topologies

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IEEE Transactions on Network and Service Management 2025年

作者： Zhang, Jialin Liang, Wei Yang, Bo Shi, Huaguang Liang, Ying-Chang Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Northwest A and F University College of Information Engineering Yangling712100 China Henan University School of Artificial Intelligence Zhengzhou450046 China University of Electronic Science and Technology of China Center for Intelligent Networking and Communications Chengdu611731 China

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot. © 2004-2012 IEEE.

关键词： Scheduling algorithms

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A Real-time Framework for UAV Indoor Self-Positioning and 3D Mapping Base on 2D Lidar, Stereo Camera and IMU

A Real-time Framework for UAV Indoor Self-Positioning and 3D...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Lebin Liang Haotian Rao Guohao Shen Can Wang Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China College of Electronic Engineering (College of Artificial Intelligence) South China Agricultural University Guangzhou China

This paper proposed a real-time Unmanned Aerial Vehicle (UAV) autonomous localization framework based on onboard sensors, including 2D Lidar, Stereo Camera and Inertial Measurement Unit (IMU), built on the open-source VINS-Fusion software to achieve real-time and drift-free UAV pose estimation. The proposed framework eliminates the motion distortion of Lidar during UAV motion by combining Visual-Inertial Odometry (VIO). It obtains global localization information of Lidar by PL-ICP algorithm based on a global point cloud map. Graph optimization method is used to fuse the local positional estimation and the global information from the Lidar, which eliminate the cumulative error caused by VIO. The feasibility of the method is verified by experiments and compared with the method of only running Visual-Inertial Navigation systems (VINS), the framework improves the accuracy of localization. In addition, we have implemented the construction of octree maps of 3D environments through the framework of this system.

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Fast automatic docking control for electric tractors

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Fundamental Research 2024年

作者： Li, Pengpeng Bai, Yu Li, Weihua Chen, Bowen You, Bindi Zhuang, Hongchao Wang, Jianfeng Gao, Haibo Deng, Zongquan School of Astronautics Harbin Institute of Technology Harbin 150001 China School of Automotive Engineering Harbin Institute of Technology (Weihai) Weihai 264201 China Yangtze River Delta HIT robot technology research institute Wuhu 241060 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China Tianjin University of Technology and Education Tianjin 300222 China

In this study, we investigate the automatic docking of an electric tractor with a semi-trailer in narrow and long-distance scenarios. We propose an automatic docking method based on a multi-sensor fusion high-precision positioning algorithm and a multi-strategy docking control algorithm. To solve the heading-angle error of the involved camera due to mechanical errors, a compensation value in the form of angle deviation is introduced. To alleviate the increase in the camera detection error with longitudinal distance, a piecewise coordinate compensation function is proposed. Repositioning is performed using light detection and ranging (Lidar) to improve the pose-perception accuracy to the millimeter level. To avoid the interference of point clouds on the relocation accuracy of Lidar, a search-and-location method that combines both small and large neighborhoods is proposed. To achieve the desired docking success rate, a multi-strategy-adjustment control algorithm is used to enable comprehensive adjustment, including approximate control of the maximum angle based on lateral deviation and fine proportional-integral-differential (PID) control based on lateral deviation and longitudinal distance, security check cycle and premature warning mechanism, and risk avoidance control. Finally, the reliability and stability of the design scheme are verified by conducting multiple experiments involving actual vehicles. © 2024

关键词： Automatic docking Electric tractor Fusion positioning Piecewise proportional-integral-differential

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Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

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Fundamental Research 2023年第6期3卷 951-959页

作者： Renhai Zhong Yue Zhu Xuhui Wang Haifeng Li Bin Wang Fengqi You Luis F.Rodríguez Jingfeng Huang K.C.Ting Yibin Ying Tao Lin College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhouZhejiang 310058China International Campus Zhejiang UniversityHainingZhejiang 314400China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China School of Geosciences and Info-Physics Central South UniversitySouth Lushan RoadChangsha 410000China NSW Department of Primary Industries Wagga Wagga Agricultural InstitutePine Gully Road Wagga WaggaNSW 2650Australia Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853USA Department of Agricultural and Biological Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Institute of Applied Remote Sensing and Information Technology Zhejiang UniversityHangzhouZhejiang 310058China Key Laboratory of On Site Processing Equipment for Agricultural Products Ministry of Agriculture and Rural AffairsHangzhouZhejiang 310058China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province HangzhouZhejiang 310058China

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food *** the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains *** this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in *** found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in *** results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

关键词： Crop yield estimation Deep Learning Long short-term memory Multi-task learning Extreme yield loss Attribution analysis

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