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arXiv 2025年

作者： Ma, Yongxin Xu, Jie Yuan, Shenghai Zhi, Tian Yu, Wenlu Zhou, Jun Xie, Lihua School of Mechanical Engineering Shandong University Jinan250061 China Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Jinan250061 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

SLAM plays a crucial role in automation tasks, such as warehouse logistics, healthcare robotics, and restaurant delivery. These scenes come with various challenges, including navigating around crowds of people, dealing with flying plastic bags that can temporarily blind sensors, and addressing reduced LiDAR density caused by cooking smoke. Such scenarios can result in over-degeneracy, causing the map to drift. To address this issue, this paper presents a multi-map LiDAR-inertial system (MM-LINS) for the first time. The front-end employs an iterated error state Kalman filter for state estimation and introduces a reliable evaluation strategy for degeneracy detection. If over-degeneracy is detected, the active map will be stored into sleeping maps. Subsequently, the system continuously attempts to construct new maps using a dynamic initialization method to ensure successful initialization upon leaving the over-degeneracy. Regarding the back-end, the Scan Context descriptor is utilized to detect inter-map similarity. Upon successful recognition of a sleeping map that shares a common region with the active map, the overlapping trajectory region is utilized to constrain the positional transformation near the edge of the prior map. In response to this, a constraint-enhanced map fusion strategy is proposed to achieve high-precision positional and mapping results. Experiments have been conducted separately on both public datasets that exhibited over-degenerate conditions and in real-world environments. These tests demonstrated the effectiveness of MM-LINS in over-degeneracy environment. Our codes are open-sourced on Github 1 Copyright © 2025, The Authors. All rights reserved.

关键词： Plastic containers

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Adapting NIST Aerial Drone Tests for Thermal Identification, Inspection, and Suppression of Wildfires

Adapting NIST Aerial Drone Tests for Thermal Identification,...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Alex Fraley Ethan Nowery McCarthy Devine Adam Jacoff Paul Y. Oh Robotics Research Engineer for the Emergency Response Robots (Ground Aerial Aquatic) Project in the Intelligent Systems Division of the Engineering Laboratory National Institute of Standards and Technology (NIST) Gaithersburg MD USA Department of Mechanical Engineering University of Nevada Las Vegas (UNLV) Las Vegas NV USA Department of Aerospace Engineering University of Colorado Boulder (CU Boulder) Boulder CO USA Project Leader for Emergency Response Robots (Ground Aerial Aquatic) in the Intelligent Systems Division of the Engineering Laboratory National Institute of Standards and Technology (NIST) Gaithersburg MD USA Faculty of Mechanical Engineering University of Nevada Las Vegas

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

The National Institute of Standards and Technology (NIST) has developed standard test methods to evaluate the capabilities of aerial drones, ground robots, and aquatic vehicles, as well as the proficiency of their operators. The aerial drone tests measure key capabilities such as maneuverability, visual acuity, and thermal acuity. With a rising demand for aerial drones among first responders, these tests are essential for assessing system performance, mitigating risk, and improving operational effectiveness to ensure the safety of responders and the public. Given the increasing prominence of wildfire threats and the widespread destruction they cause, aerial drones are being adapted to help detect, inspect, and contain them. This paper outlines how NIST-developed aerial drone tests can be adapted to simulate wildfire threats and provide a framework of reproducible tests that measure the system capabilities of aerial drones and the proficiency of operators in mitigating these threats.

关键词： Heating systems Visualization Wildfires System performance NIST Inspection Writing Object recognition Underwater vehicles Drones

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Design and Validation of a Novel Human-Machine Interface System Based on Multi-Source Data Fusion

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Pan, Lizhi Wang, Chaofan Li, Zihao Zhao, Jianchang Zhang, Chi Li, Jianmin Tianjin University Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin300350 China Tianjin University Institute of Medical Robotics and Intelligent Systems Tianjin300350 China Tsinghua University National Engineering Research Center of Neuromodulation School of Aerospace Engineering Beijing100084 China

Conventional human-machine interface (HMI) systems generally take surface electromyography as the only signal source, which often faces the problem of multi-channel crosstalk. To enhance HMI performance, this study introduces a novel three-degree-of-freedom HMI system for surgical robot control, which integrates surface electromyography signals and inertial measurement unit signals. A cost-effective wearable HMI hardware system is designed to address the high price and poor portability of existing commercial equipment, which also has a higher signal-to-noise ratio of 35.9 dB. Based on the proposed simultaneous and proportional control strategy, ten subjects were recruited to conduct an online experiment involving four upper limb postures to assess the real-time performance of the HMI system. Additionally, six able-bodied participants participated in a validation experiment to statistically verify and rigorously evaluate the implemented HMI system. The experimental results demonstrate that our HMI system can accurately decode the angles of the wrist and metacarpophalangeal joints in both single-degree-of-freedom and three-degree-of-freedom movements, achieving stable control and robust online performance. Furthermore, the proposed HMI system was applied to a flexible endoscopic robot to realize remote simultaneous and proportional control, and the results indicate that it can perform well in the practical application. The current study proves the feasibility and practicability of multi-source data fusion for HMI, providing a new direction. © 1963-2012 IEEE.

关键词： Surgical equipment

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AI Enabled Automatic Mobile Robot Intelligent Navigation in Construction with Obstacle Awareness

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IEEE Transactions on Automation Science and engineering 2025年

作者： Zhang, Yinlong Liu, Yuanhao Cui, Yunge Zeng, Ziming Liang, Wei Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China Zeekr Group Zeekr Intelligent Driving Department Shanghai110078 China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen518055 China

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot's velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation. © 2004-2012 IEEE.

关键词： Construction industry

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Towards Fast Face Image Quality Assessment via Latent Diffusion Model 18th

Towards Fast Face Image Quality Assessment via Latent Diffu...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Yan, Zheyu Zhao, Weisong Zhu, Xiangyu Gao, Li Zhang, Xiao-Yu Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China China Mobile Financial Technology Co. Ltd. Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819610679

Face recognition technology has gradually come into wide application in real life. However, in real-life unconstrained scenarios, low-quality images could be matched to unpredictable individuals, thereby affecting the stability of recognition and even causing security problems. Conducting quality assessment on face images before facial recognition can help alleviate these problems by rejecting low-quality samples. In this paper, we propose a Fast Diffusion-model-based Face Image Quality Assessment method (FDif-FIQA). It uses a diffusion model to perturb face images and measures image quality based on the similarity of features before and after the perturbation. The denoising process is transferred from the pixel space to the latent space, thereby reducing the computational cost. We evaluated the performance of this method with multiple mainstream face recognition models on various public datasets. Compared to state-of-the-art methods, our approach demonstrates highly competitive performance and significant improvements in computational efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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Deep reinforcement learning-based trajectory planning for double pendulum cranes: Design and experiments

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Mechanical systems and Signal Processing 2025年 234卷

作者： Ding, Weili Zhang, Heng Hua, Changchun Lu, Biao Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Since cranes usually possess double pendulum dynamics, the mass of the payload often changes and there are frequently lifting/lowering operations simultaneously. Moreover, most crane systems are driven by motors, whose velocities and accelerations are often limited. To solve the above problems, this paper proposes a deep reinforcement learning (DRL) reference trajectory generation method based on virtual–physical joint training. Firstly, a DRL module based on deep deterministic policy gradient (DDPG), along with a double pendulum crane dynamic model and an adaptive controller, are established within the virtual environment for training the reference trajectory. When the reward reaches the threshold, the training is switched to the physical environment to further optimize the reference trajectory, realizing the swing suppression of the hook and the payload. In addition, to satisfy the performance of the drive motors, velocity and acceleration thresholds are set to constrain the performance of the drive motors. Finally, in view of the fact that the operation process of the double pendulum crane is divided into payload transportation and payload loading/unloading, an event-triggering mechanism is designed to switch different control policies in accordance with different operation processes, thus reducing the consumption of computing resources. Through experiments on the actual double pendulum crane and comparison with the existing reference trajectories and input shapers, the superiority of this method is demonstrated. © 2025 Elsevier Ltd

关键词： Model reference adaptive control

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Sketch-SparseNet: Sparse convolution framework for sketch recognition

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Pattern Recognition 2025年 167卷

作者： Yang, Jingru Wang, Jin Zhou, Yang Lu, Guodong Sun, Yu Yu, Huan Fang, Heming Li, Zhihui He, Shengfeng Zhejiang University State Key Laboratory of Fluid Power & Mechatronic Systems Hangzhou Zhejiang310027 China Zhejiang University Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Hangzhou Zhejiang310027 China Robotics Research Center of Yuyao City Yuyao Ningbo Zhejiang315400 China Zhejiang Sealand Technology Co. Yuhang District Hangzhou Zhejiang311100 China Singapore Management University 178903 Singapore

In free-hand sketch recognition, state-of-the-art methods often struggle to extract spatial features from sketches with sparse distributions, which are characterized by significant blank regions devoid of informative content. To address this challenge, we introduce a novel framework for sketch recognition, termed Sketch-SparseNet. This framework incorporates an advanced convolutional component: the Sketch-Driven Dilated Deformable Block (SD3B). This component excels at extracting spatial features and accurately recognizing free-hand sketches with sparse distributions. The SD3B component innovatively bridges gaps in the blank areas of sketches by establishing spatial relationships among disconnected stroke points through adaptive reshaping of convolution kernels. These kernels are deformable, dilatable, and dynamically positioned relative to the sketch strokes, ensuring the preservation of spatial information from sketch points. Consequently, Sketch-SparseNet extracts a more accurate and compact representation of spatial features, enhancing sketch recognition performance. Additionally, we introduce the SmoothAlign loss function, which minimizes the disparity between the output features of parallel SD3B and CNNs, facilitating effective feature fusion. Extensive evaluations on the QuickDraw-414k and TU-Berlin datasets highlight our method's state-of-the-art performance, achieving accuracies of 79.52% and 85.78%, respectively. To our knowledge, this work represents the first application of a sparse convolution framework that substantially alleviates the adverse effects of sparse sketch points. The codes are available at https://***/kingbackyang/Sketch-SparseNet. © 2025

关键词： Character recognition

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Embedding Soft Material Channels for Tactile Sensing of Complex Surfaces—Light Propagation Modeling

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IEEE Sensors Journal 2025年第10期25卷 17230-17238页

作者： Hu, Jian Chen, Jian Back, Junghwan Zhao, Tianrui Dasgupta, Prokar Liu, Hongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Haptron Scientific Ltd. Shenzhen China School of Biomedical Engineering and Imaging Sciences King’s College London LondonSE1 7EH United Kingdom King’s College London MRC Centre for Transplantation NIHR Biomedical Research Centre LondonSE1 7EH United Kingdom Guy’s and St. Thomas’ NHS Foundation Trust LondonSE1 7EH United Kingdom

In previous work, "embedding softness into structure enabling distributed tactile sensing of high-order curved surfaces" (ESSENCE) technology, a general principle for accurate and robust tactile sensing on arbitrary surfaces, has been developed. Each tactel consists of a soft material sensing channel, a light source, and an optical receiver. Force applied on the tactel is transformed through the channel to cause an axial protrusion of the soft material. By measuring the change in light intensity caused by the protrusion, the magnitude of the force can be determined. However, multiple receivers share a single light source, resulting in lighting conditions in the region of interest (ROI) to vary from place to place, which in turn affects the consistency of sensing performance. To overcome this challenge, a freeform reflective funnel of the light source that is coated with a layer of high reflective material is designed to reshape the light distribution. Theoretical analysis and experimental results show that the proposed method improves the sensing consistency. For a 3 × 3 array-type sensor, the standard deviations of the force measuring range, linearity, and sensitivity before and after using the freeform surface are 0.29 and 0.08 N, 4.58% and 4.04%, and 0.016 and 0.007 N-1 , respectively. © 2001-2012 IEEE.

关键词： Light sources

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