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Navigation Command Matching for vision-based Autonomous Driving

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Navigation Command Matching for Vision-based Autonomous Driv...

IEEE International Conference on robotics and Automation (ICRA)

作者： Yuxin Pan Jianru Xue Pengfei Zhang Wanli Ouyang Jianwu Fang Xingyu Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China SenseTime Computer Vision Research Group University of Sydney Australia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Learning an optimal policy for autonomous driving task to confront with complex environment is a long- studied challenge. Imitative reinforcement learning is accepted as a promising approach to learn a robust driving policy through expert demonstrations and interactions with environments. However, this model utilizes non-smooth rewards, which have a negative impact on matching between navigation commands and trajectory (state-action pairs), and degrade the generalizability of an agent. Smooth rewards are crucial to discriminate actions generated from sub-optimal policy. In this paper, we propose a navigation command matching (NCM) model to address this issue. There are two key components in NCM, 1) a matching measurer produces smooth navigation rewards that measure matching between navigation commands and trajectory; 2) attention-guided agent performs actions given states where salient regions in RGB images (i.e. roadsides, lane markings and dynamic obstacles) are highlighted to amplify their influence on the final model. We obtain navigation rewards and store transitions to replay buffer after an episode, so NCM is able to discriminate actions generated from suboptimal policy. Experiments on CARLA driving benchmark show our proposed NCM outperforms previous state-of-the- art models on various tasks in terms of the percentage of successfully completed episodes. Moreover, our model improves generalizability of the agent and obtains good performance even in unseen scenarios.

关键词： Navigation Task analysis Trajectory Learning (artificial intelligence) Autonomous vehicles Smoothing methods Current measurement

Autonomous mapping of underwater 3-d structures: From view planning to execution

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IEEE robotics and Automation Letters 2018年第3期3卷 1965-1971页

作者： Palomeras, Narcis Hurtos, Natalia Carreras, Marc Ridao, Pere Computer Vision and Robotics Group University of Girona Girona17003 Spain

Three-dimensional mapping of submerged industrial facilities or underwater sites with complex geometries is still a challenge for nowadays autonomous underwater vehicles (AUVs), which are restricted to more simple survey missions. This letter presents a complete framework to plan and execute autonomous missions to map a region of interest, coping with the particularities of the underwater domain. The proposed solution consists of the following steps: First, if a previous model is not available, a preliminary bathymetry of the area to be covered is gathered and an octree representation is created. Next, using this a priori model, an offline view-planner computes a minimum set of sensor configurations to cover the volume of interest by taking into account the mapping sensor and the vehicle safety constraints. It also computes the free path to be followed by the AUV to traverse all these sensor configurations. Finally, the planned mission is executed while an online simultaneous localization and mapping algorithm prevents the AUV from drifting, ensuring that the planned path is correctly followed. Preliminary results regarding the view planner have been obtained in a shipwreck site with the Girona 500 AUV, while results involving the whole framework have been achieved using the Gazebo simulator and a realistic AUV model equipped with a multibeam sonar mounted on a pan & tilt unit. © 2016 IEEE.

关键词： Autonomous underwater vehicles

Errata: Mathematical Modeling and Designing a Heavy Hybrid-Electric Quadcopter, Controlled by Flaps

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Unmanned Systems 2024年第1期12卷 183-183页

作者： Mohammad Sadeq Ale Isaac Ahmed Refaat Ragab Enrique Caballero Garcés Marco Andres Luna Pablo Flores Peña Pascual Campoy Cervera Drone Hopper Company Spain Computer Vision and Aerial Robotics Group at Universidad Politécnica de Madrid (CVAR-UPM) at Centre for Automation and Robotics C.A.R. (UPM-CSIC) Spain University of Carlos III of Madrid Spain UC3M and Aerodynamics Engineer at Drone Hopper Spain

Accurate mapping and planning for autonomous racing

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arXiv 2020年

作者： Andresen, Leiv Brandemuehl, Adrian Hönger, Alex Kuan, Benson Vödisch, Niclas Blum, Hermann Reijgwart, Victor Bernreiter, Lukas Schaupp, Lukas Chung, Jen Jen Bürki, Mathias Oswald, Martin R. Siegwart, Roland Gawel, Abel Autonomous Systems Lab Eth Zürich Switzerland Computer Vision and Geometry Group Eth Zürich Switzerland Sevensense Robotics Ag

关键词： Pipelines

WaveCNet: Wavelet integrated CNNs to suppress aliasing effect for noise-robust image classification

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arXiv 2021年

作者： Li, Qiufu Shen, Linlin Guo, Sheng Lai, Zhihui Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China MyBank Ant Group Hangzhou310012 China

Though widely used in image classification, convolutional neural networks (CNNs) are prone to noise interruptions, i.e. the CNN output can be drastically changed by small image noise. To improve the noise robustness, we try to integrate CNNs with wavelet by replacing the common down-sampling (maxpooling, strided-convolution, and average pooling) with discrete wavelet transform (DWT). We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet). During the down-sampling, WaveCNets apply DWT to decompose the feature maps into the low-frequency and high-frequency components. Containing the main information including the basic object structures, the low-frequency component is transmitted into the following layers to generate robust high-level features. The high-frequency components are dropped to remove most of the data noises. The experimental results show that WaveCNets achieve higher accuracy on ImageNet than various vanilla CNNs. We have also tested the performance of WaveCNets on the noisy version of ImageNet, ImageNet-C and six adversarial attacks, the results suggest that the proposed DWT/IDWT layers could provide better noise-robustness and adversarial robustness. When applying WaveCNets as backbones, the performance of object detectors (i.e., faster R-CNN and RetinaNet) on COCO detection dataset are consistently improved. We believe that suppression of aliasing effect, i.e. separation of low frequency and high frequency information, is the main advantages of our approach. The code of our DWT/IDWT layer and differentWaveCNets are available at https://***/CVISZU/ WaveCNet. © 2021, CC BY.

关键词： Signal sampling

Direct sparse mapping

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arXiv 2019年

作者： Zubizarreta, Jon Aguinaga, Iker Montiel, J.M.M. Computer Vision and Robotics Group at Ceit I3A Universidad de Zaragoza

Photometric bundle adjustment, PBA, accurately estimates geometry from video. However, current PBA systems have a temporary map that cannot manage scene reobservations. We present, DSM, a full monocular visual SLAM based on PBA. Its persistent map handles reobservations, yielding the most accurate results up to date on EuRoC for a direct method. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

OTE: Optimal Trustworthy EdgeAI solutions for smart cities

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OTE: Optimal Trustworthy EdgeAI solutions for smart cities

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Vasileios Mygdalis Lorenzo Carnevale Jose Ramiro Martí nez-De-Dios Dmitriy Shutin Giovanni Aiello Massimo Villari Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mathematical and Computer Science Physics and Hearth Sciences University of Messina Messina Italy Gruppo Nazionale per il Calcolo Scientifico (GNCS) Istituto Nazionale di Alta Matematica (INdAM) &#x201C F. Severi&#x201D Rome Italy Robotics Vision and Control Group University of Seville Seville Spain Institute of Communications and Navigation German Aerospace Center (DLR) Wessling Germany Research and Development Lab. Engineering Ingegneria Informatica S.p.A Rome Italy

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

关键词： Cloud computing Privacy Smart cities Pipelines Semantics Robot sensing systems Software

PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

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arXiv 2024年

作者： Bai, Jieyun Zhou, Zihao Ou, Zhanhong Koehler, Gregor Stock, Raphael Maier-Hein, Klaus Elbatel, Marawan Martí, Robert Li, Xiaomeng Qiu, Yaoyang Gou, Panjie Chen, Gongping Zhao, Lei Zhang, Jianxun Dai, Yu Wang, Fangyijie Silvestre, Guénolé Curran, Kathleen Sun, Hongkun Xu, Jing Cai, Pengzhou Jiang, Lu Lan, Libin Ni, Dong Zhong, Mei Chen, Gaowen Campello, Víctor M. Lu, Yaosheng Lekadir, Karim Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand Heidelberg Germany Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Group University of Girona Girona Spain Co. LTD Beijing China College of Artificial Intelligence Nankai University Tianjin China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Medicine University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China School of Computer Science & Engineering Chongqing University of Technology Chongqing China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China NanFang Hospital of Southern Medical University Guangzhou China Zhujiang Hospital of Southern Medical University Guangzhou China Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain Institute The University of Auckland Private Bag 92019 Auckland1142 New Zealand

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging. © 2024, CC0.

关键词： Ultrasonic imaging

Bitcoin Price Forecasting: A Comparative Study Between Statistical and Machine Learning Methods

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Bitcoin Price Forecasting: A Comparative Study Between Stati...

International Conference on computer & Information Science (ICCIS)

作者： Waddah Waheeb Habib Shah Mohammed Jabreel Domenec Puig School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Bukit Jalil Malaysia College of Computer Science King Khalid University Abha Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona Spain

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154688

This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods.

关键词： Forecasting Bitcoin Training data Smoothing methods Training Time series analysis Support vector machines