检索结果-内蒙古大学图书馆

learning Sequential Descriptors for Sequence-Based visual Place Recognition

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 10383-10390页

作者： Mereu, Riccardo Trivigno, Gabriele Berton, Gabriele Masone, Carlo Caputo, Barbara Politecn Torino Visual & Multimodal Appl Learning Lab Dept Control & Comp Engn I-10138 Turin Italy Consorzio Interuniv Nazl Informat CINI I-00185 Rome Italy

In robotics, visual place recognition (VPR) is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This work proposes a taxonomy of the architectures used to learn sequential descriptors for VPR, highlighting different mechanisms to fuse the information from the individual images. This categorization is supported by a complete benchmark of experimental results that provides evidence of the strengths and weaknesses of these different architectural choices. The analysis is not limited to existing sequential descriptors, but we extend it further to investigate the viability of Transformers instead of CNN backbones. We further propose a new ad-hoc sequence-level aggregator called SeqVLAD, which outperforms prior state of the art on different datasets. The code is available at https://***/vandal-vpr/vg-transformers.

关键词： deep learning for visual perception localization representation learning visual learning

来源：评论

学校读者我要写书评

暂无评论

Realtime Global Attention Network for Semantic Segmentation

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 1574-1580页

作者： Mo, Xi Chen, Xiangyu Univ Kansas Sch Engn Lawrence KS 66049 USA

In this letter, we proposed an end-to-end realtime global attention neural network (RGANet) for the challenging task of semantic segmentation. Different from the encoding strategy deployed by self-attention paradigms,the proposed global attention module encodes global attention via depthwise convolution and affine transformations. The integration of these global attention modules into a hierarchical architecture maintains high inferential performance. In addition, an improved evaluation metric, namely MGRID, is proposed to alleviate the negative effect of non-convex. widely scattered ground-truth areas. Results from extensive experiments on state-of-the-art architectures for suction region segmentation manifest the leading performance of proposed approaches for robotic monocular visual perception.

关键词： Object detection segmentation and categorization deep learning for visual perception perception for grasping and manipulation

来源：评论

学校读者我要写书评

暂无评论

Multi-Robot Collaborative perception With Graph Neural Networks

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2289-2296页

作者： Zhou, Yang Xiao, Jiuhong Zhou, Yue Loianno, Giuseppe NYU Brooklyn NY 11201 USA

Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents. To enhance the autonomous robot decision-making process and situational awareness, multi-robot systems have to coordinate their perception capabilities to collect, share, and fuse environment information among the agents efficiently to obtain context-appropriate information or gain resilience to sensor noise or failures. In this letter, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience to sensor failures and disturbances. We show that the proposed framework can address multi-view visual perception problems such as monocular depth estimation and semantic segmentation. Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including images corrupted by heavy noise and camera occlusions or failures.

关键词： Aerial systems applications deep learning for visual perception

来源：评论

学校读者我要写书评

暂无评论

Image-to-Image Translation-Based Data Augmentation for Robust EV Charging Inlet Detection

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 3726-3733页

作者： Bang, Yeonjun Lee, Yeejin Kang, Byeongkeun Seoul Natl Univ Sci & Technol Dept Elect Engn Seoul 01811 South Korea Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul 01811 South Korea

This work addresses the task of electric vehicle (EV) charging inlet detection for autonomous EV charging robots. Recently, automated EV charging systems have received huge attention to improve users' experience and to efficiently utilize charging infrastructures and parking lots. However, most related works have focused on system design, robot control, planning, and manipulation. Towards robust EV charging inlet detection, we propose a new dataset (EVCI dataset) and a novel data augmentation method that is based on image-to-image translation where typical image-to-image translation methods synthesize a new image in a different domain given an image. To the best of our knowledge, the EVCI dataset is the first EV charging inlet dataset. For the data augmentation method, we focus on being able to control synthesized images' captured environments (e.g., time, lighting) in an intuitive way. To achieve this, we first propose the environment guide vector that humans can intuitively interpret. We then propose a novel image-to-image translation network that translates a given image towards the environment described by the vector. Accordingly, it aims to synthesize a new image that has the same content as the given image while looking like captured in the provided environment by the environment guide vector. Lastly, we train a detection method using the augmented dataset. Through experiments on the EVCI dataset, we demonstrate that the proposed method outperforms the state-of-the-art methods. We also show that the proposed method is able to control synthesized images using an image and environment guide vectors.

关键词： Electric vehicle charging Robots Object detection Location awareness Training data Training Fasteners Computer vision for automation data sets for robotic vision deep learning for visual perception

来源：评论

学校读者我要写书评

暂无评论

V2X-Sim: Multi-Agent Collaborative perception Dataset and Benchmark for Autonomous Driving

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 10914-10921页

作者： Li, Yiming Ma, Dekun An, Ziyan Wang, Zixun Zhong, Yiqi Chen, Siheng Feng, Chen NYU Brooklyn NY 11201 USA Univ Southern Calif Los Angeles CA 91103 USA Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai 200240 Peoples R China Shanghai AI Lab Shanghai 200240 Peoples R China

Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving. However, the lack of a public dataset significantly restricts the research progress of collaborative perception. To fill this gap, we present V2X-Sim, a comprehensive simulated multi-agent perception dataset for V2X-aided autonomous driving. V2X-Sim provides: (1) multi-agent sensor recordings from the road-side unit (RSU) and multiple vehicles that enable collaborative perception, (2) multi-modality sensor streams that facilitate multi-modality perception, and (3) diverse ground truths that support various perception tasks. Meanwhile, we build an open-source testbed and provide a benchmark for the state-of-the-art collaborative perception algorithms on three tasks, including detection, tracking and segmentation. V2X-Sim seeks to stimulate collaborative perception research for autonomous driving before realistic datasets become widely available.

关键词： deep learning for visual perception multi-robot systems data sets for robotic vision

来源：评论

学校读者我要写书评

暂无评论

deep Measurement Updates for Bayes Filters

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第1期7卷 414-421页

作者： Pankert, Johannes Minniti, Maria Vittoria Wellhausen, Lorenz Hutter, Marco Swiss Fed Inst Technol Robot Syst Lab CH-8001 Zurich Switzerland

Measurement update rules for Bayes filters often contain hand-crafted heuristics to compute observation probabilities for high-dimensional sensor data, like images. In this work, we propose the novel approach deep Measurement Update (DMU) as a general update rule for a wide range of systems. DMU has a conditional encoder-decoder neural network structure to process depth images as raw inputs. Even though the network is trained only on synthetic data, the model shows good performance at evaluation time on real-world data. With our proposed training scheme primed data training, we demonstrate how the DMU models can be trained efficiently to be sensitive to condition variables without having to rely on a stochastic information bottleneck. We validate the proposed methods in multiple scenarios of increasing complexity, beginning with the pose estimation of a single object to the joint estimation of the pose and the internal state of an articulated system. Moreover, we provide a benchmark against Articulated Signed Distance Functions(A-SDF) on the RBO dataset as a baseline comparison for articulation state estimation.

关键词： Sensor fusion deep learning for visual perception deep learning methods

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Moving Vehicle Detection From Audio-visual Cues

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 7415-7422页

作者： Zuern, Jannik Burgard, Wolfram Univ Freiburg Dept Comp Sci D-79115 Freiburg Germany Univ Nuremberg Dept Comp Sci & Engn D-91054 Erlangen Germany

Robust detection of moving vehicles is a critical task for any autonomously operating outdoor robot or self-driving vehicle. Mast modern approaches for solving this task rely on training image-based detectors using large-scale vehicle detection datasets such as nuScenes or the Waymo Open Dataset. Providing manual annotations is an expensive and laborious exercise that does not scale well in practice. To tackle this problem, we propose a self-supervised approach that leverages audio-visual cues to detect moving vehicles in videos. Our approach employs contrastive learning for localizing vehicles in images from corresponding pairs of images and recorded audio. In extensive experiments carried out with a real-world dataset, we demonstrate that our approach provides accurate detections of moving vehicles and does not require manual annotations. We furthermore show that our model can he used as a teacher to supervise an audio-only detection model. This student model is invariant to illumination changes and thus effectively bridges the domain gap inherent to models leveraging exclusively vision as the predominant modality.

关键词： Computer vision for transportation deep learning for visual perception representation learning

来源：评论

学校读者我要写书评

暂无评论

VMV-GCN: Volumetric Multi-View Based Graph CNN for Event Stream Classification

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 1976-1983页

作者： Xie, Bochen Deng, Yongjian Shao, Zhanpeng Liu, Hai Li, Youfu City Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan 430079 Peoples R China

Event cameras can perceive pixel-level brightness changes to output asynchronous event streams, and have notable advantages in high temporal resolution, high dynamic range and low power consumption for challenging vision tasks. To apply existing learning models on event data, many researchers integrate sparse events into dense frame-based representations which can work with convolutional neural networks directly. Although these works achieve high performance on event-based classification, their models need lots of parameters to process dense event frames which do not fit with the sparsity of event data. To utilize the sparse nature of events, we propose a voxel-wise graph learning model (VMV-GCN) for spatio-temporal feature learning on event streams. Specifically, we design the volumetric multi-view fusion module (VMVF) to extract spatial and temporal information from views of voxelized event data. Then we take representative event voxels as vertices and use a novel dual-graph construction strategy to connect them. By aggregating neighborhood information based on relationships of vertices, the proposed dynamic neighborhood feature learning module (DNFL) can capture discriminative spatio-temporal features on dynamically updated graphs. Experiments show that our method achieves state-of-the-art performance with low model complexity on event-based classification tasks, such as object classification and action recognition.

关键词： deep learning for visual perception object detection segmentation and categorization

来源：评论

学校读者我要写书评

暂无评论

Striving for Less: Minimally-Supervised Pseudo-Label Generation for Monocular Road Segmentation

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 10628-10634页

作者： Robinet, Francois Akl, Yussef Ullah, Kaleem Nozarian, Farzad Mueller, Christian Frank, Raphael Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-4365 Esch Sur Alzette Luxembourg Univ Saarland D-66123 Saarbrucken Germany German Res Ctr Artificial Intelligence DFKI D-66123 Saarbrucken Germany

Identifying traversable space is one of the most important problems in autonomous robot navigation and is primarily tackled using learning-based methods. To alleviate the prohibitively high annotation-cost associated with labeling large and diverse datasets, research has recently shifted from traditional supervised methods to focus on unsupervised and semi-supervised approaches. This work focuses on monocular road segmentation and proposes a practical, generic, and minimally-supervised approach based on task-specific feature extraction and pseudo-labeling. Building on recent advances in monocular depth estimation models, we process approximate dense depth maps to estimate pixel-wise road-plane distance maps. These maps are then used in both unsupervised and semi-supervised road segmentation scenarios. In the unsupervised case, we propose a pseudo-labeling pipeline that reaches state-of-the-art Intersection-over-Union (IoU), while reducing complexity and computations compared to existing approaches. We also investigate a semi-supervised extension to our method and find that even minimal labeling efforts can greatly improve results. Our semi-supervised experiments using as little as 1% and 10% of ground truth data, yield models scoring 0.9063 and 0.9332 on the IoU metric respectively. These results correspond to a comparative performance of 95.9% and 98.7% of a fully-supervised model's IoU score, which motivates a pragmatic approach to labeling.

关键词： AI-based methods computer vision for transportation deep learning for visual perception

来源：评论

学校读者我要写书评

暂无评论

SDFEst: Categorical Pose and Shape Estimation of Objects From RGB-D Using Signed Distance Fields

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9597-9604页

作者： Bruns, Leonard Jensfelt, Patric KTH Royal Inst Technol Div Robot Percept & Learning RPL SE-10044 Stockholm Sweden

Rich geometric understanding of the world is an important component of many robotic applications such as planning and manipulation. In this paper, we present a modular pipeline for pose and shape estimation of objects from RGB-D images given their category. The core of our method is a generative shape model, which we integrate with a novel initialization network and a differentiable renderer to enable 6D pose and shape estimation from a single or multiple views. We investigate the use of discretized signed distance fields as an efficient shape representation for fast analysis-by-synthesis optimization. Our modular framework enables multi-view optimization and extensibility. We demonstrate the benefits of our approach over state-of-the-art methods in several experiments on both synthetic and real data. We open-source our approach at https://***/roym899/sdfest.

关键词： RGB-D perception deep learning for visual perception

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：