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LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM 48

LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDA...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Zhang, Letian Wang, Jinping Jie, Lu Chen, Nanjie Tan, Xiaojun Duan, Zhifei Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518100 China The Automotive Technology Center XPeng Inc. Guangzhou510640 China

ISBN: (纸本)9781728163277

Existing LiDAR odometry strategies match a new scan iteratively with previous fixed-pose scans, gradually accumulating errors. Furthermore, as an effective joint optimization mechanism, bundle adjustment (BA) cannot be directly introduced into odometry due to the intensive computation of global landmarks. Therefore, this paper designs a landmark map for bundle adjustment odometry (LMBAO) in LiDAR SLAM. First, an active landmark maintenance strategy is developed to obtain a local map of limited size that enables real-time BA. Specifically, this paper keeps entire stable landmarks on the map instead of just their feature points in the sliding window and timely deletes inactive landmarks. Next, unlike visual marginalization to approximate the Gaussian distribution, and a direct and efficient marginalization strategy is performed to retain the scans outside the window to greatly simplifying the computation. Experiments show the effectiveness of LMBAO in outdoor driving. © 2023 ieee.

关键词： Optical radar

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Spotforming by NMF Using Multiple Microphone Arrays

Spotforming by NMF Using Multiple Microphone Arrays

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ieee/RSJ international conference on intelligent Robots and systems (IROS)

作者： Kagimoto, Yasuhiro Itoyama, Katsutoshi Nishida, Kenji Nakadai, Kazuhiro Tokyo Inst Technol Sch Engn Dept Syst & Control Engn Meguro Ku 2-12-1 Ookayama Tokyo 1528552 Japan Honda Res Inst Japan Co Ltd 8-1 Honcho Wako Saitama 3510188 Japan

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Sound source separation is a method to extract a target sound source from a mixture of various sound sources and noises. One of the typical sound source separation methods is beamforming, which can separate sound sources by direction based on the phase difference between channels from the recorded signal of a microphone array, a multi-channel recording system. However, beamforming is a direction-based method and cannot separate multiple sources in the same direction. In this paper, we propose a method for separating sources in the same direction using multiple microphone arrays. The proposed method performs beamforming using multiple microphone arrays and extracts only the target sound source from the separated sound by the Non-negative Matrix Factorization (NMF), thus reducing the influence of other sources in the same direction. In this paper, to investigate the effectiveness of the proposed method, experiments were conducted assuming the presence of another sound source in the same direction from an arbitrary microphone array. The results show that the proposed method outperforms the delay-sum method in a simulation environment. In addition, experiments were conducted in a real environment to verify the effect of reverberation.

关键词： Phased arrays Source separation Array signal processing Microphone arrays Recording Reverberation intelligent robots

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HRIxD: End-Effector Specialised in Drumming for Performing Robots 25

HRIxD: End-Effector Specialised in Drumming for Performing R...

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ACM/ieee international conference on Human-Robot Interaction (HRI)

作者： Khaja Ahmed Shaik Eduardo Benitez Sandoval Shengyuan Xie Francisco Cruz School of Mechanical and Manufacturing Engineering University of New South Wales Sydney Australia School of Art and Design University of New South Wales Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Escuela de Ingeniería Universidad Central de Chile Santiago Chile

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Human-robot interaction encompasses various applications, from simple tasks to advanced complex functionalities. This research introduces a specialised robotics end effector specially modelled with auditory processing for drumming along-side humans. This system interprets auditory vocal input to generate precise and adaptive drumming patterns. Utilising precision motor control and real-time signal processing to replicate drumming with human-like characteristics with precision. This work demonstrates collaboration between performing arts and robotics, demonstrating the potential of intelligent systems to interact with human creativity in real-time. The specialised end effector utilises precise motor control with motor drivers and a belt mechanism to regulate the speed and direction of the drumstick, enabling rapid, high-speed strokes to strike the drum 4 to 5 times per second. By integrating audio perception with mechanical execution, this research advances the state of the art and establishes a new framework for incorporating robotics into the creative and artistic domain.

关键词： Motor drives Human-robot interaction Collaboration signal processing Motors End effectors Real-time systems intelligent systems Robots Creativity

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Non-Destructive Testing of Rebars in Concrete 5

Non-Destructive Testing of Rebars in Concrete

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5th international conference on intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Wu, Xiaoxuan Han, Xiao Yang, Feng Qiao, Xu Zhang, Fu Xu, Xueqiang Beijing China State Key Laboratory of Coal Resources and Safety Mining Beijing China

ISBN: (纸本)9798350336030

Ground penetrating radar (GPR) technology is widely used in the detection and positioning of rebars in concrete, and the distribution of rebars is the key to the quality of structures. At present, the positioning of rebars by GPR depends on the hyperbola fitting after threshold segmentation of B-SCAN. In the case of complex B-SCAN, the existing methods have poor positioning effect on the apex of the hyperbola. At the same time, it cannot meet the requirements for real-time positioning of rebars at the *** paper proposes a CUDS-based (Column Up and Down Scan) location method for the vertex of the hyperbola formed by the reabr. By detecting the ROI at the top of the hyperbola formed by the rebar, the symmetry axis of the hyperbola is restricted to corresponding to the wave interval for the vertex of A-SCAN. Then use CUDS to realize the precise positioning of the vertex position of the hyperbola. In order to be able to apply on the mobile terminal with poor computing resources, the lightweight processing of pruning and knowledge distillation is performed on the ROI detection model. This paper combines the detection method of rebar and deploys it on the mobile terminal. In the actual detection of the physical model, the positioning accuracy of the rebar reaches 1.6mm, which is higher than the existing method C3, which verifies the effectiveness of the results of this paper. © 2023 ieee.

关键词： Ground penetrating radar systems

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Learning Generalizable Tool Use with Non-Rigid Grasp-Pose Registration 19

Learning Generalizable Tool Use with Non-Rigid Grasp-Pose Re...

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19th ieee international conference on Automation Science and Engineering, CASE 2023

作者： Mosbach, Malte Behnke, Sven University of Bonn The Autonomous Intelligent Systems Group Germany

ISBN: (纸本)9798350320695

Tool use, a hallmark feature of human intelligence, remains a challenging problem in robotics due the complex contacts and high-dimensional action space. In this work, we present a novel method to enable reinforcement learning of tool use behaviors. Our approach provides a scalable way to learn the operation of tools in a new category using only a single demonstration. To this end, we propose a new method for generalizing grasping configurations of multi-fingered robotic hands to novel objects. This is used to guide the policy search via favorable initializations and a shaped reward signal. The learned policies solve complex tool use tasks and generalize to unseen tools at test time. Visualizations and videos of the trained policies are available at https://***/generalizable-tool-use. © 2023 ieee.

关键词： Reinforcement learning

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Adaptive J-Wave Detection Architecture for Online BCG-Complex Recognition on FPGA 29

Adaptive J-Wave Detection Architecture for Online BCG-Comple...

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29th ieee international conference on Electronics, Circuits and systems (ieee ICECS)

作者： Kulau, Ulf Richter, Christoph Rust, Jochen Hamburg Univ Technol Dept Smart Sensors Hamburg Germany DSI Aerosp Technol GmbH Bremen Germany

ISBN: (数字)9781665488235

ISBN: (纸本)9781665488235

In this paper a novel hardware architecture for high-accuracy and near-sensor BCG complex recognition is presented. Main contribution of our work is the implementation of an adaptive method for J-wave peak detection on FPGA enabling reliable online waveform monitoring. Also, to further increase the overall signal quality, Chebyshev-based filtering is installed, leading to a smoother signal progression. The evaluation results highlight our approach as a well suited solution for online BCG complex recognition in resource constraint environments, as detection rates between 95.31% and 100% can be achieved considering human bodies in a resting position.

关键词： SCG BCG digital signal processing peak detection medical devices

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A Smart Agriculture Friendly intelligent Flying Robot Design to Identify Plant Leaf Diseases by using Digital Image processing with Learning Approach

A Smart Agriculture Friendly Intelligent Flying Robot Design...

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2023 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavitha, M.S. Harikrishnan, R. Shankar, S. Vijay Krishnakumari, S. Sherly, S. Irin R.M.K.Engineering College Department ECE Chennai India S.A. Engineering College Department of ECE Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Loyola ICAM College of Engineering and Technology Department of ECE Chennai India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350319200

Smart agriculture practices have become crucial in meeting the growing global food demand while minimizing resource use. In this context, the development of intelligent systems and automation plays a pivotal role. This paper introduces a novel solution: a smart agriculture-friendly intelligent flying robot equipped with cutting-edge digital image processing techniques and a learning approach to identify plant leaf diseases. The suggested system merges state-of-the-art robotics and aerial technology with advanced machine learning techniques, specifically the VGG-SVM model. The flying robot is equipped with a high-resolution camera to capture images of plant leaves in real-time, and the VGG-SVM model processes these images for disease detection. VGG offers deep feature extraction capabilities, while SVM provides accurate classification, making the model adept at discerning healthy from diseased leaves. Through digital image processing, the robot captures high-resolution images of plant leaves, which are subsequently subjected to advanced machine learning algorithms. These algorithms analyze and classify the images, providing real-time insights into the health of crops. The combination of precision aerial data collection and machine learning ensures early disease detection and targeted intervention, optimizing crop yield, reducing resource consumption, and ultimately contributing to sustainable agriculture. Proposed model excels with the highest accuracy of 0.97, indicating an overall high rate of correct predictions. © 2023 ieee.

关键词： Deep Learning Drones Plant Leaf Diseases SVM VGG

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Unsupervised Muscle Fatigue Clustering Based on sEMG signals using DeepCNN Autoencoders 11

Unsupervised Muscle Fatigue Clustering Based on sEMG Signals...

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11th RSI international conference on robotics and Mechatronics, ICRoM 2023

作者： Javanbakht, Mohammad Javad Fouji, Mohammad Reza Saffar, Amin Akhavan Akbarzadeh-T, Mohammad-R. Akbarzadeh, Alireza Mashhad Iran Mashhad Iran Ferdowsi University of Mashhad Center of Excellence on Soft Computing and Intelligent Information Processing Dept. of Electrical Engineering Mashhad Iran Mashhad Iran

ISBN: (纸本)9798350308105

Muscle fatigue assessment is key for optimizing physiotherapy, rehabilitation, and exercise. Muscle fatigue detection not only helps in reducing rehabilitation discomfort but also assists athletes in working out efficiently. The main motivation behind this research is to address the pressing need for a precise and reliable tool to assess muscle fatigue during physical activities. In addition to enhancing therapeutic interventions, this approach improves microtears-induced muscle strengthening by advancing the understanding of muscle fatigue levels. Previous studies have mainly employed supervised approaches to detect fatigue by processing surface electromyography (sEMG) signals through traditional methods in the Time, Frequency, and Time-Frequency domains. These approaches are vulnerable to challenges, such as signal sensitivity, noise interference, and inter-individual variability. Thus, the proposed method uses the spring energy model of the sEMG signal because of its excellent approximation with muscle characteristics. In order to handle noisy and ambiguous data, this paper employs a self-supervised Deep Convolutional Neural Network (DeepCNN) Autoencoder for feature extraction and K-means clustering due to their strong performance. Five healthy participants carried out two experiments using a knee rehabilitation robot (FUM-Physio). The results showcase a well-ordered clustering that closely mirrors the progression of muscle fatigue during these experiments. This indicates the robustness and generality of the proposed method. © 2023 ieee.

关键词： Muscle

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Research on Target Video Simulation and Simulation Technology Based on Shipborne Three-coordinate Radar System 7

Research on Target Video Simulation and Simulation Technolog...

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7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Fang, Fengtang Zhang, Zhen Xing, Yupeng Han, Lei Xu, Yue Cssc Systems Engineering Research Institute Beijing100094 China

ISBN: (纸本)9781665478571

We propose a method for generating video simulation of shipborne three-coordinate radar. First, the research work is carried out on the shipboard three-coordinate radar system, a radar video simulation device is designed to simulate the generation of radar video, and the target echo video block is analyzed. The calculation and drawing methods are based on the K-distributed sea clutter model to simulate the clutter background environment. Finally, the application effect is verified based on the radar video simulation device. © 2022 ieee.

关键词： Ships

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IPSN 2023 - Proceedings of the 2023 22nd international conference on Information processing in Sensor Networks

IPSN 2023 - Proceedings of the 2023 22nd International Confe...

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22nd ACM/ieee international conference on Information processing in Sensor Networks, IPSN 2023

ISBN: (纸本)9798400701184

The proceedings contain 58 papers. The topics discussed include: POS: an operator scheduling framework for multi-model inference on edge intelligent computing;CoEdge: a cooperative edge system for distributed real-time deep learning tasks;PointSplit: towards on-device 3D object detection with heterogenous low-power accelerators;addressing practical challenges in acoustic sensing to enable fast motion tracking;CMA: cross-modal association between wearable and structural vibration signal segments for indoor occupant sensing;interpersonal distance tracking with mmWave radar and IMUs;mmRipple: communicating with mmWave radars through smartphone vibration;DeepGANTT: a scalable deep learning scheduler for backscatter networks;ARSteth: enabling home self-screening with AR-assisted intelligent stethoscopes;hydra: concurrent coordination for fault-tolerant networking;MicroDeblur: image motion deblurring on microcontroller-based vision systems;and mosaic: extremely low-resolution RFID vision for visually-anonymized action recognition.

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