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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Xiaoning Ma Francis Quek Social Robotics Laboratory Interactive and Digital Institute National University of Singapore Singapore Vision Interfaces and Systems Laboratory Center of Human Computer Interaction Virginia Polytechnic Institute and State University Blacksburg VA USA

As an important approach to ensure safety and naturalness, compliant motion is already implemented in large-size robots, but it is still an undeveloped area in child-oriented robots as it calls for a lightweight and compact solution compared with large-size robots. In this paper, we proposed the design of a social robot which aims at conducting safe and playful Human-Robot Interaction (HRI), especially with children. We built a teddy bear robot prototype based on hybrid passive-active compliant system which consists of flexible joints as passive part and compliant motion controller as active part. The compliant controller detects external perturbation through motor state variables, therefore force and torque sensors could be omitted to keep overall system compact. Experiments conducted in typical HRI scenarios showed that the hybrid passive-active compliant system enabled our robot to conduct safer and more interactive physical interaction compared with robot under traditional control method.

关键词： Robot sensing systems Torque Joints Equations Mathematical model Humans

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OPTIHROV: Optically Linked Hybrid Autonomous/Remotely Operated Vehicle, Beyond Teleoperation in a New Generation of Underwater Intervention Vehicles

OPTIHROV: Optically Linked Hybrid Autonomous/Remotely Operat...

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OCEANS

作者： Roger Pi Joan Esteba Patryk Cieslak Narcís Palomeras Pedro J. Sanz Raúl Marín Pere Ridao Computer Vision and Robotics Research Institute (VICOROB) Universitat de Girona Girona Interactive and Robotic Systems Lab (IRS-Lab) Universitat Jaume I Castelló de la Plana

OPTIHROV is a proof-of-concept project to demonstrate a telerobotic system, based on an untethered, OPTIcally-connected, Hybrid Autonomous-ROV, that aims to replace light intervention ROVs operating at close range to networked infrastructures. The system is designed to bridge the gap between current ROVs, tethered to a Docking Station, and the dream of the future Intervention Autonomous Underwater Vehicle (I-AUV). OPTIHROV is a telerobotic system to perform inspection and light intervention tasks in potentially unknown and unstructured environments. It uses human-robot interaction (HRI) methods that allow the user to remotely guide the robot by launching a set of sensory-motion primitives performed by the robot *** paper presents a proof of concept demonstration of the OPTIHROV concept, where the wireless Hybrid Remotely Operated Vehicle (HROV), located in the test tank of the Universitat de Girona (UdG), is controlled/supervised from the Universitat Jaume I (UJI) in Castelló de la Plana, about 330 km away.

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Feature based slam using side-scan salient objects

Feature based slam using side-scan salient objects

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OCEANS

作者： Josep Aulinas Xavier Lladó Joaquim Salvi Yvan R. Petillot Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Spain Oceans Systems Laboratory School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

Different underwater vehicles have been developed in order to explore underwater regions, specially those of difficult access for humans. Autonomous Underwater Vehicles (AUVs) are equipped with on-board sensors, which provide valuable information about the vehicle state and the environment. This information is used to build an approximate map of the area and estimate the position of the vehicle within this map. This is the so called Simultaneous Localization and Mapping (SLAM) problem. In this paper we propose a feature based submapping SLAM approach which uses side-scan salient objects as landmarks for the map building process. The detection of salient features in this environment is a complex task, since sonar images are noisy. We present in this paper an algorithm based on a set of image preprocessing steps and the use of a boosted cascade of Haar-like features to perform the automatic detection in side-scan images. Our experimental results show that the method produces consistent maps, while the vehicle is precisely localized.

关键词： Simultaneous localization and mapping Vehicles Feature extraction Sonar detection Object detection Sonar navigation

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PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 16th

PIPAL: A Large-Scale Image Quality Assessment Dataset for Pe...

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16th European Conference on computer vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Feature extraction for underwater visual SLAM

Feature extraction for underwater visual SLAM

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OCEANS - Europe

作者： Josep Aulinas Marc Carreras Xavier Llado Joaquim Salvi Rafael Garcia Ricard Prados Yvan R. Petillot Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Spain Oceans Systems Laboratory School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

Detecting and selecting proper landmarks is a key issue to solve Simultaneous Localization and Mapping (SLAM). In this work, we present a novel approach to perform this landmark detection. Our approach is based on using three sources of information: 1) three-dimensional topological information from SLAM; 2) context information to characterize regions of interest (RoI); and 3) features extracted from these RoIs. Topological information is taken from the SLAM algorithm, i.e. the three-dimensional approximate position of the landmark with a certain level of uncertainty. Contextual information is obtained by segmenting the image into background and RoIs. Features extracted from points of interest are then computed by using common feature extractors such as SIFT and SURF. This information is used to associate new observations with known landmarks obtained from previous observations. The proposed approach is tested under a real unstructured underwater environment using the SPARUS AUV. Results demonstrate the validity of our approach, improving map consistency.

关键词： Feature extraction Simultaneous localization and mapping Cameras Robustness Vehicles Uncertainty Optical imaging

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Scene Consistency Representation Learning for Video Scene Segmentation

arXiv

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arXiv 2022年

作者： Wu, Haoqian Chen, Keyu Luo, Yanan Qiao, Ruizhi Ren, Bo Liu, Haozhe Xie, Weicheng Shen, Linlin Computer Vision Institute Shenzhen University China Tencent YouTu Lab Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China KAUST Saudi Arabia

A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a scene starts and ends. To this end, we propose an effective Self-Supervised Learning (SSL) framework to learn better shot representations from unlabeled long-term videos. More specifically, we present an SSL scheme to achieve scene consistency, while exploring considerable data augmentation and shuffling methods to boost the model generalizability. Instead of explicitly learning the scene boundary features as in the previous methods, we introduce a vanilla temporal model with less inductive bias to verify the quality of the shot features. Our method achieves the state-of-the-art performance on the task of Video Scene Segmentation. Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods. The code is made available. © 2022, CC BY.

关键词： Semantics

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Image saliency detection method based on multi-feature maps fusion 4

Image saliency detection method based on multi-feature maps ...

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4th International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Li, Xiaoli Liu, Yunpeng Zhao, Huaici Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510683242

In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics." © 2024 SPIE.

关键词： Feature extraction

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

arXiv

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arXiv 2024年

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computer Science University of Nottingham China

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt Generator (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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DualQuat-LOAM: LiDAR Odometry and Mapping parametrized on Dual Quaternions

arXiv

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arXiv 2024年

作者： Velasco-Sánchez, Edison P. Recalde, Luis F. Li, Guanrui Candelas-Herias, Francisco A. Puente-Mendez, Santiago T. Torres-Medina, Fernando AUROVA Group of Automation Robotics and Computer Vision University of Alicante San Vicente del Raspeig S/N Alicante03690 Spain Worcester Polytechnic Institute Worcester01609 United States

This paper reports on a novel method for LiDAR odometry estimation, which completely parameterizes the system with dual quaternions. To accomplish this, the features derived from the point cloud, including edges, surfaces, and Stable Triangle Descriptor (STD), along with the optimization problem, are expressed in the dual quaternion set. This approach enables the direct combination of translation and orientation errors via dual quaternion operations, greatly enhancing pose estimation, as demonstrated in comparative experiments against other state-of-the-art methods. Our approach reduced drift error compared to other LiDAR-only-odometry methods, especially in scenarios with sharp curves and aggressive movements with large angular displacement. DualQuat-LOAM is benchmarked against several public datasets. In the KITTI dataset it has a translation and rotation error of 0.79% and 0.0039°/m, with an average run time of 53 ms. © 2024, CC BY-NC-ND.

关键词： robotics

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Accuracy estimation of a new omnidirectional 3D vision sensor

Accuracy estimation of a new omnidirectional 3D vision senso...

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IEEE International Conference on Image Processing

作者： R. Orghidan J. Salvi E.M. Mouaddib Institute of Informatics and Applications Computer Vision and Robotics Group University of Girona Catalonia Spain Centre de Robotique Électrotechnique et Automatique Université de Picardie Jules-Verne Amiens France

We present a computer vision system that associates omnidirectional vision with structured light with the aim of obtaining depth information for a 360 degrees field of view. The approach proposed in this article combines an omnidirectional camera with a panoramic laser projector. The article shows how the sensor is modelled and its accuracy is proved by means of experimental results. The proposed sensor provides useful information for robot navigation applications, pipe inspection, 3D scene modelling etc.

关键词： Cameras Robot vision systems Robot sensing systems Layout Mirrors Application software computer vision Navigation robotics and automation Laser modes

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