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AirLine: Efficient Learnable Line Detection with Local Edge Voting

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arXiv 2023年

作者： Lin, Xiao Wang, Chen College of Computing Georgia Institute of Technology AtlantaGA30332 United States Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-level precision, yet with a 3 − 80× runtime acceleration compared to other learning-based methods, which is critical for low-power robots. Copyright © 2023, The Authors. All rights reserved.

关键词： robotics

PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking

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PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Micha Heiß Jakob Grimm Hansen Dengyun Li Michał Kozłowski Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic systems in large-scale industrial sites in mind. The ROI are determined through an object detection network trained to detect moving human beings. The method detects and removes humans from feature extraction, predicting their potential future trajectory. This is done by omitting a specific ROI from extraction, deemed to be occluded in consecutive time steps. Two masking methods -static object and moving object trajectories - are proposed. This approach improves tracking accuracy and the performance of SLAM by removing the dynamic features from the reference for tracking and loop closures. The method is tested on data collected in a laboratory environment and compared against a state-of-the-art ground truth system. The validation data was collected from real-time experiments which aimed at simulating the typical human worker behaviours in industrial environments using an unmanned aerial vehicle (UAV). This study illustrates the advantages of the proposed method over earlier approaches, even with a highly dynamic camera setup on a UAV working in challenging environments.

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CAIS: Culvert Autonomous Inspection Robotic System

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CAIS: Culvert Autonomous Inspection Robotic System

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

作者： Chuong Phuoc Le Pratik Walunj An Duy Nguyen Yongyi Zhou Binh Nguyen Thang Nguyen Anton Netchaev Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA Department of Engineering Texas A&M University–Corpus Christi Corpus Christi TX USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Culverts, essential components of drainage systems, require regular inspection to ensure optimal functionality. However, culvert inspections pose numerous challenges, including accessibility, manpower, defect localization, and reliance on superficial assessments. To address these challenges, we propose a novel Culvert Autonomous Inspection Robotic System (CAIS) equipped with advanced sensing and evaluation capabilities. Our solution integrates an RGBD camera, deep learning, lighting systems, and non-destructive evaluation (NDE) techniques to enable accurate and comprehensive condition assessments. We present a pioneering Partially Observable Markov Decision Process (POMDP) framework to resolve uncertainty in autonomous inspections, especially in confined and unstructured environments like culverts or tunnels. The framework outputs detailed 3D maps highlighting visual defects and NDE condition assessments, demonstrating consistent and reliable performance in both indoor and outdoor scenarios. Additionally, we provide an open-source implementation of our framework on GitHub, contributing to the advancement of autonomous inspection technology and fostering collaboration within the research community. Source codes are available * .

关键词： Visualization Uncertainty Three-dimensional displays Source coding Markov decision processes Robot vision systems Inspection Sensors Reliability Software development management

Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor with MOSFET Structure

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Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor wit...

IEEE SENSORS

作者： Kamalesh Tripathy Dhanranjan Kumar Karthick Thiyagarajan Mitradip Bhattacharjee Department of Electrical Engineeringand Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In this paper, we present a flexible capacitive tactile sensor based on a polydimethylsiloxane (PDMS) elastomer, which is well-suited for a wide range of applications. The sensor incorporates an n-channel depletion-mode MOSFET to achieve an extended gate architecture. To enhance the sensing performance, TiO 2 nanoparticles are incorporated as nanofillers, resulting in an improved dielectric material ideal for highly sensitive capacitive pressure and tactile sensors. The 99PDMS-01TiO 2 nanocomposite (comprising 99 wt % PDMS and 1 wt % TiO 2 ) is utilized for promising flexible tactile sensor applications due to the synergistic effects between the PDMS matrix and TiO 2 nanoparticles, which enhance both the mechanical and sensing properties of the composite. The fabricated sensor exhibits a sensitivity of $\mathbf{30} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ within the range of 0-10.5 kPa and a sensitivity of $\mathbf{9} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ in the range of 10.5-30 kPa.

关键词： Nanoparticles MOSFET Sensitivity Nanocomposites Capacitors Tactile sensors Logic gates Robustness Sensors Titanium dioxide

FDCT: Fast Depth Completion for Transparent Objects

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arXiv 2023年

作者： Li, Tianan Chen, Zhehan Liu, Huan Wang, Chen The School of Mechanical Engineering University of Science and Technology Beijing Beijing100083 China The Department of Electrical and Computer Engineering McMaster University Canada The Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain computationally intensive and often fail to meet the real-time requirements of low-power robotic platforms. Additionally, most methods are designed for opaque objects and struggle with transparent objects due to the special properties of reflection and refraction. To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation. To leverage local information and avoid overfitting issues when integrating it with global information, we design a new fusion branch and shortcuts to exploit low-level features and a loss function to suppress overfitting. This results in an accurate and user-friendly depth rectification framework which can recover dense depth estimation from RGB-D images alone. Extensive experiments demonstrate that FDCT can run about 70 FPS with a higher accuracy than the state-of-the-art methods. We also demonstrate that FDCT can improve pose estimation in object grasping tasks. The source code is available at https://***/Nonmy/FDCT. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers

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Assisted Physical Interaction: Autonomous Aerial Robots with...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Andrea Berra Viswa Narayanan Sankaranarayanan Achilleas Santi Seisa Julien Mellet Udayanga G.W.K.N. Gamage Sumeet Gajanan Satpute Fabio Ruggiero Vincenzo Lippiello Silvia Tolu Matteo Fumagalli George Nikolakopoulos Miguel Ángel Trujillo Soto Guillermo Heredia CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Electrical and Space Engineering Robotics and AI Team Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

关键词： Visualization Pose estimation Neural networks Lighting Object detection Autonomous aerial vehicles Aircraft navigation

Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context

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arXiv 2023年

作者： Song, Yicheng Gao, Shuyong Xing, Haozhe Cheng, Yiting Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Keenon Robotics Co. Ltd. Shanghai China Academy for Engineering & Technology Fudan University Shanghai China

Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods for USOD have been proposed as a promising alternative. However, current solutions rely heavily on noisy handcraft labels and fail to mine rich semantic information from deep features. In this paper, we propose a self-supervised end-to-end salient object detection framework via top-down context. Specifically, motivated by contrastive learning, we exploit the self-localization from the deepest feature to construct the location maps which are then leveraged to learn the most instructive segmentation guidance. Further considering the lack of detailed information in deepest features, we exploit the detail-boosting refiner module to enrich the location labels with details. Moreover, we observe that due to lack of supervision, current unsupervised saliency models tend to detect non-salient objects that are salient in some other samples of corresponding scenarios. To address this widespread issue, we design a novel Unsupervised Non-Salient Suppression (UNSS) method developing the ability to ignore non-salient objects. Extensive experiments on benchmark datasets demonstrate that our method achieves leading performance among the recent end-to-end methods and most of the multi-stage solutions. The code is available. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Challenges for Monocular 6D Object Pose Estimation in robotics

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arXiv 2023年

作者： Thalhammer, Stefan Bauer, Dominik Hönig, Peter Weibel, Jean-Baptiste García-Rodríguez, José Vincze, Markus The Industrial Engineering Departent UAS Technikum Vienna Austria The Automation and Control Institute TU Wien Austria The Columbia Artificial Intelligence and Robotics Lab Columbia University United States The Department of Computer Technology University of Alicante Spain

Object pose estimation is a core perception task that enables, for example, object manipulation and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference make monocular approaches especially well suited for robotics applications. We observe that previous surveys on establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved. © 2023, CC BY.

关键词： Robots

Robots as Mental Well-being Coaches: Design and Ethical Recommendations

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arXiv 2022年

作者： Axelsson, Minja Spitale, Micol Gunes, Hatice AFAR Lab [Affective Intelligence and Robotics Laboratory Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Politecnico di Milano Milan Italy Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

The last decade has shown a growing interest in robots as well-being coaches. However, insightful guidelines for the design of robots as coaches to promote mental well-being have not yet been proposed. This paper details design and ethical recommendations based on a qualitative analysis drawing on a grounded theory approach, which was conducted with a three-step iterative design process which included user-centered design studies involving robotic well-being coaches, namely: (1) a user-centred design study conducted with 11 participants consisting of both prospective users who had participated in a Brief Solution-Focused Practice study with a human coach, as well as coaches of different disciplines, (2) semi-structured individual interview data gathered from 20 participants attending a Positive Psychology intervention study with the robotic well-being coach Pepper, and (3) a user-centred design study conducted with 3 participants of the Positive Psychology study as well as 2 relevant well-being coaches. After conducting a thematic analysis and a qualitative analysis, we collated the data gathered into convergent and divergent themes, and we distilled from those results a set of design guidelines and ethical considerations. Our findings can inform researchers and roboticists on the key aspects to take into account when designing robotic mental well-being coaches. © 2022, CC BY.

关键词： User centered design