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Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping

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arXiv 2025年

作者： Wang, Dong Haag, Hannes Herraez, Daniel Casado May, Stefan Stachniss, Cyrill Nüchter, Andreas Informatics XVII – Robotics Julius-Maximilians-Universität Würzburg Germany Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg Institute of Technology Georg Simon Ohm Germany CARIAD SE University of Bonn Germany Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Simultaneous localization and mapping (SLAM) is a critical capability for autonomous systems. Traditional SLAM approaches, which often rely on visual or LiDAR sensors, face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show that our system significantly outperforms state-of-the-art radar-SLAM and LiDAR-SLAM frameworks in terms of accuracy and robustness. To encourage further research, the code of our Doppler-SLAM and our dataset are available at: https://***/Wayne-DWA/Doppler-SLAM. © 2025, CC BY-NC-ND.

关键词： Mapping

In-flight Efficient Controller Auto-tuning using a Pair of UAVs

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In-flight Efficient Controller Auto-tuning using a Pair of U...

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Wojciech Giernacki Dariusz Horla Martin Saska Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Poznan Poland Faculty of Electrical Engineering Czech Technical University in Prague Prague 6 Czech Republic

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

In the paper, a pair of auto-tuning methods for fixed-parameter controllers is presented, in application to multirotor unmanned aerial vehicles (UAVs) control. In both cases, the automatized process of searching the best altitude controller parameters is carried out with the use of a modified golden-search method, for a selected cost function, during the flight of a pair of UAVs. All the calculations are performed in real-time in the iterative manner using only basic sensory information available concerning current altitude information for a pair of UAVs. The auto-tuning process of the controller is characterized by neglectfully low computational demand, and the parameters are obtained rapidly with no dynamic model of a UAV needed. In both methods, by using a pair of UAVs in tuning process, the level of control performance can be increased, what has been proved by means of multiple outdoor experiments. The first method increases precision of the obtained controller parameters by averaging sensory information over a pair of UAVs, whereas in the second, by exchanging measurement information between the units, the search space is explored faster. The latter is of special importance when seeking the best controller parameters, what is especially expected when a limited experiment duration of multirotor UAVs is taken into account.

关键词： Process control Unmanned aerial vehicles Space exploration Safety Vehicle dynamics Task analysis Tuning

Teleoperation of Humanoid Robots: A Survey

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arXiv 2023年

作者： Darvish, Kourosh Penco, Luigi Ramos, Joao Cisneros, Rafael Pratt, Jerry Yoshida, Eiichi Ivaldi, Serena Pucci, Daniele Artificial and Mechanical Intelligence Center for Robotics and Intelligent Systems Istituto Italiano di Tecnologia Genoa Italy Florida Institute for Human and Machine Cognition 40 S Alcaniz St PensacolaFL32502 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States Department of Information Technology and Human Factors Tsukuba Japan Inria Loria Université de Lorraine CNRS Nancy France Department of Applied Electronics Faculty of Advanced Engineering Tokyo University of Science Japan

Teleoperation of humanoid robots enables the integration of the cognitive skills and domain expertise of humans with the physical capabilities of humanoid robots. The operational versatility of humanoid robots makes them the ideal platform for a wide range of applications when teleoperating in a remote environment. However, the complexity of humanoid robots imposes challenges for teleoperation, particularly in unstructured dynamic environments with limited communication. Many advancements have been achieved in the last decades in this area, but a comprehensive overview is still missing. This survey paper gives an extensive overview of humanoid robot teleoperation, presenting the general architecture of a teleoperation system and analyzing the different components. We also discuss different aspects of the topic, including technological and methodological advances, as well as potential applications. A web-based version of the paper can be found at https://***/. © 2023, CC BY.

关键词： Remote control

EFaR 2023: Efficient Face Recognition Competition

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

Extensions of the open-source framework Aerostack 3.0 for the development of more interactive flights between UAVs

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Extensions of the open-source framework Aerostack 3.0 for th...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Wojciech Giernacki Jacek Cieślak Martin Molina Pascual Campoy Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznań Poland Department of Artificial Intelligence Technical University of Madrid (UPM) Madrid Spain Computer Vision and Aerial Robotics (CVAR) Centre for Automation and Robotics Technical University of Madrid (UPM) Madrid Spain

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

The basis for properly verified R&D works is to provide reliable prototyping tools at three most important stages: computer simulation, laboratory tests and real-world experiments. In the laboratory-limited conditions, particular importance is attributed to the first two stages, especially in the context of the safety development of autonomous flights of unmanned aerial vehicle (UAV) groups in various missions. The open-source framework Aerostack support those needs and its effectiveness has been proven in the International Micro Air Vehicle Indoor Competitions (IMAV 2013, 2016, 2017) and Mohammed Bin Zayed International robotics Challenge (MBZIRC 2020). In the paper, the exemplary functionalities for the new version of Aerostack Version 3.0 Distribution Sirocco (Aerial robotics framework for the industry), extended additionally with a library of new behaviors, are presented. The mission of UAVs can be developed fast and effectively in order to conduct test flights with real drones in lab, before one will decide to fly autonomously outdoor. The representative results obtained for low-cost *** 2.0 UAV models in two missions, are presented. The first mission is autonomous patrolling the area by a pair of UAVs, the second - intercepting the intruder in guarded area by the guard UAV.

关键词： Robots Trajectory Drones Libraries Software Computer architecture

Active Disturbance Rejection Control of High-Order Flat Underactuated Systems: Mass-Spring Benchmark Problem 20th

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Active Disturbance Rejection Control of High-Order Flat Unde...

20th Polish Control Conference, PCC 2020

作者： Madonski, Rafal Ramirez-Neria, Mario Giernacki, Wojciech Energy Electricity Research Center International Energy College Jinan University ZhuhaiGuangdong Province519070 China Mechatronics Department Universidad Politecnica del Valle de Mexico UPVM Tultitlan de Mariano Escobedo Fuentes del Valle54910 Mexico Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan60-965 Poland

ISBN: (纸本)9783030509354

A custom active disturbance rejection control (ADRC) is proposed for trajectory tracking in uncertain underactuated systems. By utilizing a particular property of the differentially flat plant model, a robust output-feedback controller with a novel cascade of extended state observers (ESO) is introduced. It effectively deals with the problem of governing high-order plants without over-amplification of the measurement noise, typically seen in conventional single high-gain observer-centered control approaches. The proposed solution is based on full utilization of the information already available about the governed system, without necessity for additional measurement devices. In order to be easily implementable, it assumes only limited knowledge of the system model and is expressed in an industry familiar error-based form with a straightforward tuning method. A representable two-mass three-spring benchmark problem is used throughout the paper to convey the proposed idea and evaluate it experimentally on a laboratory testbed. © 2020, Springer Nature Switzerland AG.

关键词： Disturbance rejection

EFaR 2023: Efficient Face Recognition Competition

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EFaR 2023: Efficient Face Recognition Competition

IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

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Active disturbance rejection control design with suppression of sensor noise effects in application to DC-DC buck power converter

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arXiv 2020年

作者： Lakomy, Krzysztof Madonski, Rafal Dai, Bin Yang, Jun Kicki, Piotr Ansari, Maral Li, Shihua The Poznaá Universitńy of Technology Poznan60-965 Poland The Energy Electricity Research Center International Energy College Jinan University Zhuhai519070 China The School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Nanjing210096 China The Institute of Robotics and Machine Intelligence Poznań University of Technology Poznań60-965 Poland The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia

The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this work, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a DC-DC buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise over-amplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Spurious signal noise

Why is the Winner the Best?

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Why is the Winner the Best?

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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