2020 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第5页

Steerable Burrowing Robot: Design, Modeling and Experiments 操纵性挖洞机器人:设计、建模与实验

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196648

M. Barenboim, A. Degani

引用次数: 7

Singularity analysis and reconfiguration mode of the 3-CRS parallel manipulator 3-CRS并联机构奇异性分析及重构模式

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197337

C. Bouzgarrou, A. Koessler, N. Bouton

引用次数: 1

Day and Night Collaborative Dynamic Mapping in Unstructured Environment Based on Multimodal Sensors 基于多模态传感器的非结构化环境昼夜协同动态映射

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197072

Yufeng Yue, Chule Yang, Jun Zhang, Mingxing Wen, Zhenyu Wu, Haoyuan Zhang, Danwei W. Wang

引用次数: 17

Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles 平面地面车辆高效全局最优无对应视觉里程计

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196595

Ling Gao, Junyan Su, Jiadi Cui, Xiangchen Zeng, Xin-Zhong Peng, L. Kneip

{"title":"Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles","authors":"Ling Gao, Junyan Su, Jiadi Cui, Xiangchen Zeng, Xin-Zhong Peng, L. Kneip","doi":"10.1109/ICRA40945.2020.9196595","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196595","url":null,"abstract":"The motion of planar ground vehicles is often non-holonomic, and as a result may be modelled by the 2 DoF Ackermann steering model. We analyse the feasibility of estimating such motion with a downward facing camera that exerts fronto-parallel motion with respect to the ground plane. This turns the motion estimation into a simple image registration problem in which we only have to identify a 2-parameter planar homography. However, one difficulty that arises from this setup is that ground-plane features are indistinctive and thus hard to match between successive views. We encountered this difficulty by introducing the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. The solution relies on the branch-and-bound optimisation technique. Through the low-dimensional parametrisation, a derivation of tight bounds, and an efficient implementation, we demonstrate how this technique is eventually amenable to accurate real-time motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation. Our results on real data finally demonstrate a significant advantage over the more traditional, correspondence-based hypothesise-and-test schemes.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"40 1","pages":"2696-2702"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87997262","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: 7

Reliable Data Association for Feature-Based Vehicle Localization using Geometric Hashing Methods 基于几何哈希方法的基于特征的车辆定位可靠数据关联

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196601

Isabell Hofstetter, Michael Sprunk, Florian Ries, M. Haueis

{"title":"Reliable Data Association for Feature-Based Vehicle Localization using Geometric Hashing Methods","authors":"Isabell Hofstetter, Michael Sprunk, Florian Ries, M. Haueis","doi":"10.1109/ICRA40945.2020.9196601","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196601","url":null,"abstract":"Reliable data association represents a main challenge of feature-based vehicle localization and is the key to integrity of localization. Independent of the type of features used, incorrect associations between detected and mapped features will provide erroneous position estimates. Only if the uniqueness of a local environment is represented by the features that are stored in the map, the reliability of localization is enhanced.In this work, a new approach based on Geometric Hashing is introduced to the field of data association for feature-based vehicle localization. Without any information on a prior position, the proposed method allows to efficiently search large map regions for plausible feature associations. Therefore, odometry and GNSS-based inputs can be neglected, which reduces the risk of error propagation and enables safe localization.The approach is demonstrated on approximately 10min of data recorded in an urban scenario. Cylindrical objects without distinctive descriptors, which were extracted from LiDAR data, serve as localization features. Experimental results both demonstrate the feasibility as well as limitations of the approach.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"34 1","pages":"1322-1328"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88028224","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

Securing Industrial Operators with Collaborative Robots: Simulation and Experimental Validation for a Carpentry task 用协作机器人保护工业操作员:木工任务的仿真和实验验证

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197161

Nassim Benhabib, V. Padois, D. Daney

引用次数: 1

Unsupervised Geometry-Aware Deep LiDAR Odometry 无监督几何感知深度激光雷达里程计

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197366

Younggun Cho, Giseop Kim, Ayoung Kim

{"title":"Unsupervised Geometry-Aware Deep LiDAR Odometry","authors":"Younggun Cho, Giseop Kim, Ayoung Kim","doi":"10.1109/ICRA40945.2020.9197366","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197366","url":null,"abstract":"Learning-based ego-motion estimation approaches have recently drawn strong interest from researchers, mostly focusing on visual perception. A few learning-based approaches using Light Detection and Ranging (LiDAR) have been re-ported; however, they heavily rely on a supervised learning manner. Despite the meaningful performance of these approaches, supervised training requires ground-truth pose labels, which is the bottleneck for real-world applications. Differing from these approaches, we focus on unsupervised learning for LiDAR odometry (LO) without trainable labels. Achieving trainable LO in an unsupervised manner, we introduce the uncertainty-aware loss with geometric confidence, thereby al-lowing the reliability of the proposed pipeline. Evaluation on the KITTI, Complex Urban, and Oxford RobotCar datasets demonstrate the prominent performance of the proposed method compared to conventional model-based methods. The proposed method shows a comparable result against SuMa (in KITTI), LeGO-LOAM (in Complex Urban), and Stereo-VO (in Oxford RobotCar). The video and extra-information of the paper are described in https://sites.google.com/view/deeplo.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"2145-2152"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86482774","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: 53

Cooperative Perception and Localization for Cooperative Driving 协同驾驶的协同感知与定位

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197463

Aaron Miller, Kyungzun Rim, Parth Chopra, Paritosh Kelkar, M. Likhachev

{"title":"Cooperative Perception and Localization for Cooperative Driving","authors":"Aaron Miller, Kyungzun Rim, Parth Chopra, Paritosh Kelkar, M. Likhachev","doi":"10.1109/ICRA40945.2020.9197463","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197463","url":null,"abstract":"Fully autonomous vehicles are expected to share the road with less advanced vehicles for a significant period of time. Furthermore, an increasing number of vehicles on the road are equipped with a variety of low-fidelity sensors which provide some perception and localization data, but not at a high enough quality for full autonomy. In this paper, we develop a perception and localization system that allows a vehicle with low-fidelity sensors to incorporate high-fidelity observations from a vehicle in front of it, allowing both vehicles to operate with full autonomy. The resulting system generates perception and localization information that is both low-noise in regions covered by high-fidelity sensors and avoids false negatives in areas only observed by low-fidelity sensors, while dealing with latency and dropout of the communication link between the two vehicles. At its core, the system uses a set of Extended Kalman filters which incorporate observations from both vehicles’ sensors and extrapolate them using information about the road geometry. The perception and localization algorithms are evaluated both in simulation and on real vehicles as part of a full cooperative driving system.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"57 1","pages":"1256-1262"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86105011","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: 21

Magnetic Sensor Based Topographic Localization for Automatic Dislocation of Ingested Button Battery 基于磁传感器的扣式电池自动错位定位

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196546

Jialun Liu, Hironari Sugiyama, T. Nakayama, S. Miyashita

引用次数: 5

Robot Plan Model Generation and Execution with Natural Language Interface* 基于自然语言接口的机器人计划模型生成与执行*

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196987

Kyon-Mo Yang, Kap-Ho Seo, S. Kang, Yoonseob Lim

{"title":"Robot Plan Model Generation and Execution with Natural Language Interface*","authors":"Kyon-Mo Yang, Kap-Ho Seo, S. Kang, Yoonseob Lim","doi":"10.1109/ICRA40945.2020.9196987","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196987","url":null,"abstract":"Verbal interaction between a human and a robot may play a key role in conveying suitable directions for a robot to achieve the goal of a user’s request. However, a robot may need to correct task plans or make new decisions with human help, which would make the interaction inconvenient and also increase the interaction time. In this paper, we propose a new verbal interaction-based method that can generate plan models and execute proper actions without human involvement in the middle of performing a task by a robot. To understand the verbal behaviors of humans when giving instructions to a robot, we first conducted a brief user study and found that a human user does not explicitly express the required task. To handle such unclear instructions by a human, we propose two different algorithms that can generate a component of new plan models based on intents and entities parsed from natural language and can resolve the unclear entities existed in human instructions. An experimental scenario with a robot, Cozmo, was tried in the lab environment to test whether or not the proposed method could generate an appropriate plan model. As a result, we found that the robot could successfully accomplish the task following human instructions and also found that the number of interactions and components in the plan model could be reduced as opposed to the general reactive plan model. In the future, we are going to improve the automated process of generating plan models and apply various scenarios under different service environments and robots.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"22 1","pages":"6973-6978"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82646002","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