2022 International Conference on Robotics and Automation (ICRA)最新文献_第8页

RepAr-Net: Re-Parameterized Encoders and Attentive Feature Arsenals for Fast Video Denoising re - net:用于快速视频去噪的重新参数化编码器和关注特征库

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812394

S. Sharan, Adithya K. Krishna, A. S. Rao, V. Gopi

引用次数: 2

Globally Optimal Relative Pose Estimation for Multi-Camera Systems with Known Gravity Direction 已知重力方向的多相机系统全局最优相对姿态估计

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812380

Qianliang Wu, Yaqing Ding, Xinlei Qi, Jin Xie, Jian Yang

{"title":"Globally Optimal Relative Pose Estimation for Multi-Camera Systems with Known Gravity Direction","authors":"Qianliang Wu, Yaqing Ding, Xinlei Qi, Jin Xie, Jian Yang","doi":"10.1109/icra46639.2022.9812380","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9812380","url":null,"abstract":"Multiple-camera systems have been widely used in self-driving cars, robots, and smartphones. In addition, they are typically also equipped with IMUs (inertial measurement units). Using the gravity direction extracted from the IMU data, the y-axis of the body frame of the multi-camera system can be aligned with this common direction, reducing the original three degree-of-freedom(DOF) relative rotation to a single DOF one. This paper presents a novel globally optimal solver to compute the relative pose of a generalized camera. Existing optimal solvers based on LM (Levenberg-Marquardt) method or SDP (semidefinite program) are either iterative or have high computational complexity. Our proposed optimal solver is based on minimizing the algebraic residual objective function. According to our derivation, using the least-squares algorithm, the original optimization problem can be converted into a system of two polynomials with only two variables. The proposed solvers have been tested on synthetic data and the KITTI benchmark. The experimental results show that the proposed methods have competitive robustness and accuracy compared with the existing state-of-the-art solvers.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126647662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Colored Petri Net Model for Control Problem of Border Crossing Under Constraints 约束下过境控制问题的有色Petri网模型

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811549

Hela Kadri, S. C. Dutilleul, P. Bon, R. Merzouki

引用次数: 1

Dynamic Robot Chain Networks for Swarm Foraging 群体觅食的动态机器人链网络

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811625

Dohee Lee, Qi Lu, T. Au

引用次数: 2

Learning to Pick by Digging: Data-Driven Dig-Grasping for Bin Picking from Clutter 通过挖掘学习挑选:数据驱动的挖掘抓取从杂乱中挑选

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811736

Chao Zhao, Zhekai Tong, Juan Rojas, Jungwon Seo

{"title":"Learning to Pick by Digging: Data-Driven Dig-Grasping for Bin Picking from Clutter","authors":"Chao Zhao, Zhekai Tong, Juan Rojas, Jungwon Seo","doi":"10.1109/icra46639.2022.9811736","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9811736","url":null,"abstract":"We present a data-driven approach for effective bin picking from clutter. Recent bin picking solutions usually lead to a direct pinch grasp on a target object without addressing any other potential contact interaction in clutter. However, appropriate physical interaction can be essential to successful singulation and subsequent secure picking, the goal of bin picking. In this work, we contribute a framework that learns physically interactive actions for object picking end-to-end from a visual input in a self-supervised manner. The learned actions enable the robot to purposefully interact with a target object by performing a digging operation through the clutter. By leveraging a fully convolutional network (FCN), we predict picking success probabilities for a set of interactive action primitives that will in turn specify an optimal action to perform. The FCN is trained in a simulated environment through trial and error. Moreover, new datasets are collected using the latest network through iterative self-supervision. Extensive real-world bin picking experiments show the effectiveness and generalizability of the approach.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126832162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data 从轨迹数据中学习部署交通平滑巡航控制器

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811912

Nathan Lichtlé, Eugene Vinitsky, Matthew Nice, Benjamin Seibold, D. Work, A. Bayen

{"title":"Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data","authors":"Nathan Lichtlé, Eugene Vinitsky, Matthew Nice, Benjamin Seibold, D. Work, A. Bayen","doi":"10.1109/icra46639.2022.9811912","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9811912","url":null,"abstract":"Autonomous vehicle-based traffic smoothing con-trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I–24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human followers. Using policy-gradient methods with an asymmetric critic to learn the controller, we show that we are able to improve average MPG by 11% in simulation on congested trajectories. We deploy this controller to a mixed platoon of 4 autonomous Toyota RAV-4's and 7 human drivers in a validation experiment and demonstrate that the expected time-gap of the controller is maintained in the real world test. Finally, we release the driving dataset [1], the simulator, and the trained controller at https://github.com/nathanlct/trajectory-training-icra.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114118132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

On the Convergence of Multi-robot Constrained Navigation: A Parametric Control Lyapunov Function Approach 多机器人约束导航的收敛性:一种参数控制Lyapunov函数方法

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811807

Bowen Weng, Hua Chen, W. Zhang

{"title":"On the Convergence of Multi-robot Constrained Navigation: A Parametric Control Lyapunov Function Approach","authors":"Bowen Weng, Hua Chen, W. Zhang","doi":"10.1109/icra46639.2022.9811807","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9811807","url":null,"abstract":"This paper studies the distributed multi-robot constrained navigation problem. While the multi-robot collision avoidance has been extensively studied in the literature with safety being the primary focus, the individual robot's destination convergence is not necessarily guaranteed. In particular, robots may get stuck in the local equilibria or periodic orbits of the multi-robot system, some of which are practically known as the deadlock and the livelock behaviors. Inspired by the combination of Control Lyapunov Function (CLF) and Control Barrier Function (CBF) for the nonlinear system's constrained stabilization, the authors present a guaranteed safe feedback control policy with improved convergence performance. The proposed Parametric CLF (PCLF) scheme adaptively determines the appropriate CLF parameterization within the in-stantaneous feasible action space. The algorithm also induces a conditional global asymptotic convergence guarantee for multi-robot system of single-integrator dynamics, and is empirically effective for nonlinear nonholonomic vehicle model. Empiri-cally, the proposed PCLF-CBF framework exhibits superior performance than state-of-the-art methods, including its de-generated counterpart of various CLF-CBF solutions.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121077984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DKNAS: A Practical Deep Keypoint Extraction Framework Based on Neural Architecture Search 一种实用的基于神经结构搜索的深度关键点提取框架

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9812101

Li Liu, Xing Cai, Ge Li, Thomas H. Li

{"title":"DKNAS: A Practical Deep Keypoint Extraction Framework Based on Neural Architecture Search","authors":"Li Liu, Xing Cai, Ge Li, Thomas H. Li","doi":"10.1109/icra46639.2022.9812101","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9812101","url":null,"abstract":"Keypoint extraction including both keypoint detection and description is a fundamental step in a wide range of geometric multimedia applications. In recent years, many learning-based approaches for keypoint extraction emerge and achieve promising results. However, they usually design network architectures empirically and lack of considerations about the comprehensive performance, which leads to limited applications. In this paper, we propose a practical framework based on Neural Architecture Search (NAS) technology, DKNAS, which can search architectures automatically and maintain efficiency and effectiveness, simultaneously. To the best of our knowledge, the proposed framework is the first NAS framework for keypoint extraction. The evaluation on HPatches dataset shows that our method achieves state-of-the-art results in the metrics of repeatability, localization error, homography accuracy and matching scores. Besides, our model is applied to a traditional Simultaneous Localization and Mapping (SLAM) system, ORB-SLAM2, to replace the handcrafted keypoints. Experimental results demonstrate that the system adopting our model outperforms ORB-SLAM2 and some other deep keypoints enhanced systems.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116236512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reconfigurable Underactuated Adaptive Gripper Designed by Morphological Computation * 基于形态计算的可重构欠驱动自适应夹持器设计*

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811738

I. Borisov, Evgenii E. Khornutov, D.V. Ivolga, N.A. Molchanov, I.A. Maksimov, S. Kolyubin

引用次数: 2

JST: Joint Self-training for Unsupervised Domain Adaptation on 2D&3D Object Detection JST:基于二维和三维目标检测的无监督域自适应联合自训练

2022 International Conference on Robotics and Automation (ICRA) Pub Date : 2022-05-23 DOI: 10.1109/icra46639.2022.9811975

Guangyao Ding, Meiying Zhang, E. Li, Qi Hao

引用次数: 4