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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications"

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Precise Indoor Positioning System for Mobile Robots via Smoothed UWB/IMU sensor fusion 13

Precise Indoor Positioning System for Mobile Robots via Smoo...

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13th International conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Elsanhoury, Mahmoud Nieminen, Jyri Valisuo, Petri Siemuri, Akpojoto Koljonen, Janne Elmusrati, Mohammed Kuusniemi, Heidi University of Vaasa Digital Economy Platform Vaasa Finland University of Vaasa Vebic Platform Digital Economy Platform Vaasa Finland University of Vaasa School of Technology and Innovations Digital Economy Platform Vaasa Finland University of Vaasa School of Technology and Innovations Vaasa Finland University of Vaasa Digital Economy Platform School of Technology and Innovations Vaasa Finland

ISBN: (纸本)9798350320114

Indoor positioning systems (IPSs) are the foundations for all indoor location-based services and applications. In this article, we present a precise and robust IPS using ultra wide-band (UWB) technology fused with an inertial measurement unit (IMU). Both technologies are integrated to account for the non-line-of-sight (NLOS) problems arising in a dense challenging environment found within an industrial laboratory in Finland. Besides the conventional estimation techniques e.g. extended Kalman filter (EKF), we employ the Rauch-Tung-Striebel (RTS) smoothing algorithm in addition to a multivariate regression-based offset compensation method to improve the overall positioning accuracy of the system. The recommended number of distributed UWB anchors versus the coverage area is also discussed and tested in this article. The experiments were held by a patrolling mobile robot with millimeter accuracy, which acted as a ground truth reference to all used algorithms. The positioning estimation results showed a superior performance by the proposed method (UWB/IMU EKF-RTS-LR) with mean accuracy of 4.7 cm, and 9.6 cm for more than 95% of the time. © 2023 IEEE.

关键词： indoor positioning system (IPS) inertial motion unit (IMU) mobile robots sensor fusion ultra-wideband (UWB)

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Multi-sensor aided deep pose tracking 2nd

Multi-sensor aided deep pose tracking

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2nd Modeling, Estimation and Control conference (MECC)

作者： Lee, Hojun Toner, Tyler Tilbury, Dawn Barton, Kira Univ Michigan Dept Robot Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

Accurate pose estimation of nearby objects is critical for robots to dynamically interact with their surroundings. The complexity of this task has led researchers to explore deep learning methods. Nowadays, many works have solely focused on developing complicated neural network architectures to estimate pose from a simple monocular camera. However, most of these methods struggle with inherent limitations of a single sensor system, like occlusion, which are commonly encountered in mobile robotics applications. Online, occlusion-robust pose estimation is extremely important in such cases, as mobility of a robot introduces major uncertainty that complicates manipulation. Hence, we present Multi-sensor aided Deep Pose Tracking (MS-DPT), a framework for online object pose estimation to enable robust mobile manipulation. A Convolutional Neural Network (CNN) identifies key objects in an RGB-D image, from which object pose estimates are generated using a variant of the Iterative Closest Point (ICP) algorithm. An extended Kalman filter is used to fuse this pose estimate with onboard motion sensors to compensate for occlusion and robot motion during manipulation. This three stage method focuses on cohering different modalities and algorithms to improve the pose tracking stability and continuity in cases where the target object becomes heavily occluded by an obstacle or a mobile robot itself. The proposed approach accurately tracks textureless objects with high symmetries while operating at 10 FPS during experiments. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Object pose estimation Neural-network model Robot vision Data fusion Tracking

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Research on the System Design and Application of Mobile Manipulator Hybrid Robot for Transporting in Intelligent Factory

Research on the System Design and Application of Mobile Mani...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International conference on

作者： Xiaohong Qin Lianhao Zhang Bin Zhao Shenyang Institute of Science and Technology Shenyang China Northeastern University Shenyang China SIASUN Robot & Automation Co. Shenyang China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Based on the development and application status of mobile manipulator hybrid robot, this paper takes the handling process requirements of intelligent factories in China as the research objective to design and develop a composite robot platform system. The hardware system mainly includes mobile platforms, manipulators, and sensors. The software system mainly consists of control systems, software architectures, algorithm libraries and corresponding communication protocols. This platform system has the key technologies of scene information perception, mobile path planning and self-collision detection of the manipulator during the grasping process, which are necessary in engineering project applications. The corresponding innovative algorithms are embedded in it, and scene tests and effect analysis are carried out in a memory module production factory. Test results show that, compared with mainstream products and application technologies in the field, this mobile manipulator hybrid robot can use multiple sensors to obtain scene information and conduct high-precision data fusion, simultaneously meet multiple measurement indicators of mobile path planning, and quickly achieve pre-collision warning without the application of sensors, verifying the effectiveness and advancement of the independently developed platform system.

关键词： Software architecture Service robots Software algorithms Memory modules sensor fusion Manipulators Robot sensing systems Production facilities Path planning Intelligent sensors

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Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach

Uncertainty-Aware Online Extrinsic Calibration: A Conformal ...

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IEEE Workshop on applications of Computer Vision (WACV)

作者： Mathieu Cocheteux Julien Moreau Franck Davoine Universite de technologie de Compiegne CNRS Heudiasyc France CNRS INSA Lyon UCBL LIRIS UMR5205 France

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Accurate sensor calibration is crucial for autonomous systems, yet its uncertainty quantification remains under-explored. We present the first approach to integrate un-certainty awareness into online extrinsic calibration, com-bining Monte Carlo Dropout with Conformal Prediction to generate prediction intervals with a guaranteed level of coverage. Our method proposes a framework to en-hance existing calibration models with uncertainty quan-tification, compatible with various network architectures. Validated on KITTI (RGB Camera-LiDAR) and DSEC (Event Camera-LiDAR) datasets, we demonstrate effective-ness across different visual sensor types, measuring performance with adapted metrics to evaluate the efficiency and reliability of the intervals. By providing calibration pa-rameters with quantifiable confidence measures, we offer insights into the reliability of calibration estimates, which can greatly improve the robustness of sensor fusion in dy-namic environments and usefully serve the Computer Vision community.

关键词： Computer vision Visualization Uncertainty Monte Carlo methods Autonomous systems Computer network reliability sensor fusion Probabilistic logic Robustness Calibration

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Analysis of Sorting algorithms Using a WSN and Environmental Pollution Data based on FPGA 27

Analysis of Sorting Algorithms Using a WSN and Environmental...

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27th International conference on Applied Electronics (AE)

作者： Montesdeoca, Guillermo Asanza, Victor Chica, Kevin Peluffo-Ordonez, Diego H. ESPOL Escuela Super Politecn Litoral Fac Ingn Elect & Comp Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador SDAS Res Grp Smart Data Anal Syst Grp Ben Guerir 47963 Morocco Mohammed VI Polytech Univ Modeling Simulat & Data Anal MSDA Res Program Ben Guerir 47963 Morocco

ISBN: (数字)9781665494823

ISBN: (纸本)9781665494823

Wireless Snesor Network (WSN) based systems with a focus on Internet of Things (IoT) applications generate a large amount of data. Many applications that need to process data in real time make use of microcontroller-based architectures with sequential programming. Systems based on sequential programming can emulate parallelism up to a certain number of instructions, which is not the case with Field Programmable Gate Array (FPGA). The main objective of this work is to monitor a network of 40 CO2 sensors and to perform real-time sorting of all data. In addition, the run time analysis of two sorting algorithms is performed: bubble sort and insertion sort. For this purpose, an FPGA-based architecture is implemented, controlled by a finite state machine(FSM), which executes each of the sorting algorithms. The results show that the insertion sort algorithm is faster than the burbble sort, but consumes more hardware resources in the FPGA.

关键词： FPGA Sorting Algortims WSN sensor Network

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Human Activity Recognition with Smartphones using Machine Learning Algorithm

Human Activity Recognition with Smartphones using Machine Le...

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Knowledge Engineering and Communication Systems (ICKECS), International conference on

作者： K Ghamya K Prema D Abhinay Reddy K Varaprasad K Navyasri Sai S Tejesh Department of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of CSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Human Activity Recognition with Smartphones (HAR) leverages the ubiquitous nature of smartphones equipped with sensor technology to identify and classify daily human activities from the generated data. This paper explores the field of HAR, focusing on the use of smartphone sensors like magnetometers, gyroscopes, and accelerometers to capture movement patterns. This provides a comprehensive exploration of HAR, delving into methodologies such as feature extraction data acquisition, and the application of various machine learning algorithms for activity classification. The discussion encompasses challenges and limitations intrinsic to smartphone-based HAR, addressing issues like data variability and device heterogeneity. Promising research directions are highlighted, with an emphasis on context awareness, multi sensor fusion, and deep learning approaches, aiming to enhance the robustness and accuracy of activity recognition systems. The abstract concludes by emphasizing the potential applications of HAR in healthcare, where it could revolutionize patient monitoring, fitness monitoring, optimizing routines, and personalized user experiences, showeasing its role in shaping the future of health-related technologies. This model contributes valuable insights into the dynamic field of HAR, considering the synergy between smartphones, sensor technologies, and machine learning algorithms to propel innovative applications and personalized user experiences.

关键词： Knowledge engineering Patient monitoring Machine learning algorithms Magnetometers Medical services sensor fusion Propulsion

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Multiple IMU fusion Algorithm Comparison for Sounding Rocket Attitude applications 35

Multiple IMU Fusion Algorithm Comparison for Sounding Rocket...

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35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022

作者： Gómez, M.A. García, Laura Train Rico, Alberto Solera Gómez-Pérez, Ignacio Sánchez, Eusebio Valero Universidad Politécnica de Madrid Spain Universidad Carlos III de Madrid Spain Instituto Nacional de Técnica Aeroespacial Spain

ISBN: (纸本)9781713871361

Micro-electro-mechanical (MEMS) technology is known to offer a wide variety of advantages for navigation and guidance purposes in terms of cost, size and weight. However, sensor accuracy limits its applications. The use of a Redundant Inertial Measurement Unit (RIMU) configuration allows combining inertial observations not only to obtain a better performances for attitude estimation, but also to be able to detect and isolate a sensor not properly working. The output signals of uncorrelated IMU sensors can be integrated using a data fusion algorithm (e.g Extended Kalman Filter, EKF). The aim of this study is to present the implementation of several filters for an array of consumer grade IMUs placed on a "skew-redundant" configuration in a sounding rocket vehicle. The filtering method allows designing an attitude estimator capable of improving the kinematic predictions from gyroscope measurement integration by applying a correction method from accelerometer and magnetometer data. Moreover, simulations are performed to analyze the resilience and fault detection, while laboratory tests check the performance of the two proposed architectures. With the aim of testing accuracy of the algorithms for the RIMU a software simulation tool and laboratory tests are presented. © 2022 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. All rights reserved.

关键词： Extended Kalman filters

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Humanoid Robots that Move on Two Feet and Are Led by Vision Systems 3

Humanoid Robots that Move on Two Feet and Are Led by Vision ...

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3rd International conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kumar, Santosh Yadav, Satya Prakash School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jaipuria Institute of Management Department of Management Jaipur302033 India Glbitm Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

At the convergence of robotics and computer vision lies a crucial research field that focuses on the development of vision-guided walking for humanoid robots. The ability of humanoid robots to walk independently while simultaneously seeing and comprehending their surroundings via the use of visual inputs offers tremendous potential. Humanoid robots are developed to reproduce human-like qualities and skills. This abstract discusses current developments in the area of vision-guided walking for humanoid robots as well as the approaches that have been used. Through the use of computer vision methods in conjunction with walking algorithms, humanoid robots are given the capacity to traverse situations that are both complex and dynamic while exhibiting increased flexibility. For this, real-time sensing of the environment, object identification and avoidance of obstacles, as well as analysis of the terrain, are required in order to arrive at intelligent judgements while moving. Vision-guided walking not only makes robotic locomotion more secure and effective, but it also paves the way for a wide range of potential applications, including exploration, human-robot interaction, search and rescue, and many more. Visual sensing systems, sensor fusion procedures, gait generation methods, motion planning, and control strategies. These topics are discussed in relation to vision-guided walking. Different types of sensors, such as cameras, depth sensors, and LiDAR, are being investigated to see how they can best provide a holistic understanding of the surrounding environment. In addition, the abstract draws attention to the difficulties caused by sensor noise, occlusions, fluctuations in illumination, and the need for real-time processing. In places an emphasis on the role that methods of machine learning play in the improvement of vision-guided walking. The use of deep learning algorithms gives robots the ability to learn from data, hence enhancing their capacity to understand and r

关键词： Head movement in robot Humanoid robots Planning and control Proprioceptive sensors sensors Vision guided

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Improving Automatic Target Recognition (ATR) Performance with Electro Optics (EO) and Infrared (IR) sensor fusion

Improving Automatic Target Recognition (ATR) Performance wit...

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2022 IEEE Aerospace conference, AERO 2022

作者： Chen, Hai-Wen Kapadia, Ravi General Atomics Ems San DiegoCA92127 United States

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

In previous years, we developed Automatic Target Recognition (ATR) algorithms by combining layers of the open-source You Only Look Once (YOLOv2) detection model with customized Convolutional Neural Network (CNN) feature extraction layers to recognize targets in Infrared (IR) images. Our work showed that ATR for IR performed significantly better during night-time than during daytime. In this study, we demonstrate that fusing EO and IR images using pixel-based and decision-based sensor fusion can improve daytime ATR performance significantly. Traditional Automatic Target Detection (ATD) metrics do not account for misclassification while traditional target classification metrics do not count missed detections. We have developed a novel approach for evaluating ATR performance that bridges traditional target detection and target classification metrics based on an extended confusion matrix (ECM) which allows us to accurately characterize Probability of Detection (Pd), Probability of False Alarm (Pfa), and the tradeoffs between the two for ATR applications. After running the ATR detector at multiple Confidence Score thresholds, we can obtain and describe the detection performance at different Pfa levels using the Receiver Operating Characteristic (ROC) curves to show the comprehensive relationship between Pd vs. Pfa. Combining EO and IR fusion approaches with the ECM, we demonstrate improvements in Pd of 11% with pixel-based fusion and 13-17% with decision-based fusion, respectively. © 2022 IEEE.

关键词： Image enhancement

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ROS Powered Autonomous Mobile Robot for Indoor applications

ROS Powered Autonomous Mobile Robot for Indoor Applications

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International conference on

作者： Madan H T Mahesh L Mohammed Sajjad Department of EC (Advanced Communication Technology) NMAM Institute of Technology (NMAMIT) Nitte (Deemed to be University) Nitte India School of Electronics and Communication Engineering REVA University Bangalore India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In the realm of indoor robotics, navigation poses a critical challenge across service robots, humanoids, and warehouse automation. Existing techniques like line following, RFID tracking, and Aruco marker-based systems exhibit limitations in autonomy and adaptability. This has created a need for the development of an advanced and sophisticated autonomous navigation system that encompasses self-reliant perception, planning, and control capabilities. The proposed system makes use of LiDAR technology for mapping and Robot operating system for planning and control which takes place on Jetson Nano GPU platform, providing real-time processing power for environmental data. LiDAR sensors offer accurate environmental perception, obstacle detection, and adaptability to dynamic indoor settings. The system employs BLDC wheels and an aluminium base for versatility, supporting loads up to 120 kg. Additionally, it incorporates an IMU sensor with EKF sensor fusion for accurately estimating the robot's position. This flexible design has applications in various indoor robotics scenarios, ranging from complex service tasks to efficient warehouse operations. This versatile design finds applications in various indoor robotics scenarios, from complex service tasks to efficient warehouse operations.

关键词： Laser radar Navigation sensor fusion Robot sensing systems sensor systems Planning Indoor environment Mobile robots Robots Autonomous robots

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