2021 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第8页

KFS-LIO: Key-Feature Selection for Lightweight Lidar Inertial Odometry KFS-LIO:轻型激光雷达惯性里程计关键特征选择

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561324

Wei Li, Yu Hu, Yinhe Han, Xiaowei Li

引用次数: 2

Robust Semantic Map Matching Algorithm Based on Probabilistic Registration Model 基于概率配准模型的鲁棒语义映射匹配算法

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561176

Qingxiang Zhang, Meiling Wang, Yufeng Yue

{"title":"Robust Semantic Map Matching Algorithm Based on Probabilistic Registration Model","authors":"Qingxiang Zhang, Meiling Wang, Yufeng Yue","doi":"10.1109/ICRA48506.2021.9561176","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561176","url":null,"abstract":"The matching and fusing of local maps generated by multiple robots can greatly enhance the performance of relative localization and collaborative mapping. Currently, existing semantic matching methods are partly based on classical iterative closet point (ICP), which typically fail in cases with large initial error. What’s more, current semantic matching algorithms have high computation complexity in optimizing the transformation matrix. To address the challenge of map matching with large initial error, this paper proposes a novel semantic map matching algorithm with large convergence region. The key novelty of this work is the designing of the initial transformation optimization algorithm and the probabilistic registration model to increase the convergence region. To reduce the initial error before the iteration process, the initial transformation matrix is optimized by estimating the credibility of the data association. At the same time, a factor reflecting the uncertainty of the initial error is calculated and introduced to the formulation of the probabilistic registration model, thereby accelerating the convergence process. The proposed algorithm is performed on public datasets and compared with existing methods, demonstrating the significant improvement in terms of matching accuracy and robustness.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130161695","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: 3

Modeling and Control of an Untethered Magnetic Gripper 无系留磁爪的建模与控制

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561882

Yunxuan Mao, Sishen Yuan, Jiaole Wang, Jinmin Zhang, Shuang Song

引用次数: 3

Learned Uncertainty Calibration for Visual Inertial Localization 视觉惯性定位的学习不确定度标定

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561179

Stephanie Tsuei, Stefano Soatto, P. Tabuada, Mark B. Milam

引用次数: 1

A Versatile Vision-Pheromone-Communication Platform for Swarm Robotics 一种面向群体机器人的多功能视觉信息通信平台

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561911

Tian Liu, Xuelong Sun, Cheng Hu, Qinbing Fu, Shigang Yue

{"title":"A Versatile Vision-Pheromone-Communication Platform for Swarm Robotics","authors":"Tian Liu, Xuelong Sun, Cheng Hu, Qinbing Fu, Shigang Yue","doi":"10.1109/ICRA48506.2021.9561911","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561911","url":null,"abstract":"This paper describes a versatile platform for swarm robotics research. It integrates multiple pheromone communication with a dynamic visual scene along with real time data transmission and localization of multiple-robots. The platform has been built for inquiries into social insect behavior and bio-robotics. By introducing a new research scheme to coordinate olfactory and visual cues, it not only complements current swarm robotics platforms which focus only on pheromone communications by adding visual interaction, but also may fill an important gap in closing the loop from bio-robotics to neuroscience. We have built a controllable dynamic visual environment based on our previously developed ColCOSΦ (a multi-pheromones platform) by enclosing the arena with LED panels and interacting with the micro mobile robots with a visual sensor. In addition, a wireless communication system has been developed to allow transmission of real-time bi-directional data between multiple micro robot agents and a PC host. A case study combining concepts from the internet of vehicles (IoV) and insect-vision inspired model has been undertaken to verify the applicability of the presented platform, and to investigate how complex scenarios can be facilitated by making use of this platform.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278656","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: 2

Recognizing Orientation Slip in Human Demonstrations 识别人类演示中的方向滑动

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561856

Michael Hagenow, Bolun Zhang, Bilge Mutlu, Michael Gleicher, M. Zinn

引用次数: 1

Real-Time Mesh Extraction from Implicit Functions via Direct Reconstruction of Decision Boundary 基于直接重构决策边界的隐函数实时网格提取

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560749

Wataru Kawai, Yusuke Mukuta, T. Harada

{"title":"Real-Time Mesh Extraction from Implicit Functions via Direct Reconstruction of Decision Boundary","authors":"Wataru Kawai, Yusuke Mukuta, T. Harada","doi":"10.1109/ICRA48506.2021.9560749","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9560749","url":null,"abstract":"The ability to estimate 3D object shape from a single image is vital to robotics and manufacturing. For instance, it enables iterative trial-and-error in simulated environments. In single-view reconstruction, implicit functions have demonstrated superior results over traditional methods. However, implicit functions suffer from the heavy computation of mesh extraction. This is due to the indirect mesh extraction, where the number of evaluation points grows cubically with resolution. On the other hand, reducing the resolution results in the discretization error of marching cubes (MC). In this work, we aim to perform efficient and accurate mesh extraction from implicit functions. The idea is to directly reconstruct the decision boundary of implicit functions as a mesh by reverse tracing from the output. It eliminates the need for evaluating massive points and error-prone MC. Consequently, we propose implementing an implicit function via a composite function of a flow and Binary-coded Input Neural Network (BCINN). The boundary of BCINN is easily identifiable, and the flow is invertible. Owing to these properties, the decision boundary of the composite function can be directly and efficiently reconstructed. In our experiments, we demonstrate that the proposed method significantly improves runtime/memory efficiency, with results comparable to those of existing methods. Specifically, our method enables real-time high-quality mesh inference from a single image.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133884840","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

Automated Planning of Workcell Layouts Considering Task Sequences 考虑任务序列的工作单元布局的自动规划

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561831

Timo Bachmann, Korbinian Nottensteiner, M. Roa

引用次数: 2

Experimental Validation of Unsteady Wave Induced Loads on a Stationary Remotely Operated Vehicle 固定式遥控车辆非定常波诱导载荷的实验验证

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9562010

Kyle L. Walker, R. Gabl, S. Aracri, Yu Cao, A. Stokes, A. Kiprakis, F. Giorgio-Serchi

{"title":"Experimental Validation of Unsteady Wave Induced Loads on a Stationary Remotely Operated Vehicle","authors":"Kyle L. Walker, R. Gabl, S. Aracri, Yu Cao, A. Stokes, A. Kiprakis, F. Giorgio-Serchi","doi":"10.1109/ICRA48506.2021.9562010","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9562010","url":null,"abstract":"Shallow water environments pose daunting scenarios for the operation of Unmanned Underwater Vehicles (UUVs), due to significantly larger wave disturbances being present in comparison to a typical deep sea situation. Performing inspection and maintenance tasks at close quarters in these conditions requires reliable control methods robust to external disturbances, allowing accurate position and attitude control, an aspect which classical control methods are often lacking. Improved performance can be achieved through predictive control methods, however, these require accurate and time-efficient estimations of the hydrodynamic forces produced by the immediate ocean environment around the vehicle. Considering this, we present a low-order model for faster-than-real time estimation of the wave-induced hydrodynamic forces acting on a submerged vehicle in various sea state conditions. The model is thoroughly corroborated by experimental tests, performed using a Remotely Operated Vehicle (ROV) situated at shallow depth whilst subjected to realistic sea wave disturbances. Validation between simulations and the collected experimental data showed a maximum normalised mean error deviation of 0.16 and 0.27 for surge and heave forces respectively, and 0.34 for the pitching moment. This empirical evidence demonstrates that accurate predictions of wave-generated forces can be produced through low-order models at a speed suitable for incorporation within predictive control architectures.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131788723","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

ENCODE: a dEep poiNt Cloud ODometry nEtwork 编码:一个深点云里程计网络

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9562024

Yihuan Zhang, Liangbo Wang, Chen Fu, Yifan Dai, J. Dolan

{"title":"ENCODE: a dEep poiNt Cloud ODometry nEtwork","authors":"Yihuan Zhang, Liangbo Wang, Chen Fu, Yifan Dai, J. Dolan","doi":"10.1109/ICRA48506.2021.9562024","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9562024","url":null,"abstract":"Ego-motion estimation is a key requirement for the simultaneous localization and mapping (SLAM) problem. The traditional pipeline goes through feature extraction, feature matching and pose estimation, whose performance depends on the manually designed features. In this paper, we are motivated by the strong performance of deep learning methods in other computer vision and robotics tasks. We replace hand-crafted features with a neural network and directly estimate the relative pose between two adjacent scans from a LiDAR sensor using ENCODE: a dEep poiNt Cloud ODometry nEtwork. Firstly, a spherical projection of the input point cloud is performed to acquire a multi-channel vertex map. Then a multi-layer network backbone is applied to learn the abstracted features and a fully connected layer is adopted to estimate the 6-DoF ego-motion. Additionally, a map-to-map optimization module is applied to update the local poses and output a smooth map. Experiments on multiple datasets demonstrate that the proposed method achieves the best performance in comparison to state-of-the-art methods and is capable of providing accurate poses with low drift in various kinds of scenarios.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129401764","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