2019 European Conference on Mobile Robots (ECMR)最新文献_第4页

Visual-Inertial Teach & Repeat for Aerial Robot Navigation 空中机器人导航的视觉惯性教学和重复

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870926

M. Nitsche, Facundo Pessacg, Javier Civera

引用次数: 5

Trajectory Following using Nonlinear Model Predictive Control and 3D Point-Cloud-based Localization for Autonomous Driving 基于非线性模型预测控制和三维点云定位的自动驾驶轨迹跟踪

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870956

Ajish Babu, Kerim Yener Yurtdas, C. Koch, Mehmed Yüksel

{"title":"Trajectory Following using Nonlinear Model Predictive Control and 3D Point-Cloud-based Localization for Autonomous Driving","authors":"Ajish Babu, Kerim Yener Yurtdas, C. Koch, Mehmed Yüksel","doi":"10.1109/ECMR.2019.8870956","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870956","url":null,"abstract":"In autonomous driving, the trajectory follower is one of the critical controllers which should be capable of handling different driving scenarios. Most of the existing controllers are limited to a particular driving scenario and for a specific vehicle model. In this work, the trajectory follower is formulated as a nonlinear model predictive control problem and solved using the multiple-shooting trajectory optimization method, Gauss-Newton Multiple Shooting. This solver has already been used for other control applications and provides the flexibility to use different nonlinear models. The controller is tested using a retrofitted autonomous driving platform, along with the 3D point-cloud-based mapping and localization algorithms. The nonlinear model being used is a classical kinematic bicycle model. Due to the high nonlinearity between the vehicle inputs, throttle and brake, and the acceleration, the longitudinal speed control uses an additional piece-wise linear mapping. The results from the initial tests, while following a predefined trajectory on a Go-Kart test-track, are evaluated and presented here.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124007256","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

Distributed 3D TSDF Manifold Mapping for Multi-Robot Systems 多机器人系统的分布式三维TSDF流形映射

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870930

Thibaud Duhautbout, J. Moras, J. Marzat

引用次数: 10

Emergency Landing Aware Surveillance Planning for Fixed-wing Planes 固定翼飞机紧急着陆感知监视规划

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870933

Petr Váňa, J. Faigl, Jakub Sláma

引用次数: 1

Segmentation of Depth Images into Objects Based on Polyhedral Shape Class Model 基于多面体形状类模型的深度图像目标分割

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870917

R. Cupec, D. Filko, Petra Durovic

引用次数: 1

Query Generation for Resolving Ambiguity in User's Command for a Mobile Service Robot 移动服务机器人用户命令歧义消解查询生成

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870919

Kazuho Morohashi, J. Miura

引用次数: 2

Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection 基于特征变化检测的移动机器人终身自主性研究

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870940

Erik Derner, Clara Gómez, A. C. Hernández, R. Barber, R. Babuška

{"title":"Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection","authors":"Erik Derner, Clara Gómez, A. C. Hernández, R. Barber, R. Babuška","doi":"10.1109/ECMR.2019.8870940","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870940","url":null,"abstract":"Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ultimate goal of life-long autonomy. In computer vision-based methods employed on mobile robots, such as localization or navigation, one of the major issues is the dynamics of the scenes. The autonomous operation of the robot may become unreliable if the changes that are common in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects on the desks and other changes make many conventional methods fail. To deal with that, we present a novel method for change detection based on the similarity of local visual features. The core idea of the algorithm is to distinguish important stable regions of the scene from the regions that are changing. To evaluate the change detection algorithm, we have designed a simple visual localization framework based on feature matching and we have performed a series of real-world localization experiments. The results have shown that the change detection method substantially improves the accuracy of the robot localization, compared to using the baseline localization method without change detection.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127169908","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

Performance of RGB-D camera for different object types in greenhouse conditions 温室条件下不同目标类型下RGB-D相机的性能

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870935

Ola Ringdahl, P. Kurtser, Y. Edan

{"title":"Performance of RGB-D camera for different object types in greenhouse conditions","authors":"Ola Ringdahl, P. Kurtser, Y. Edan","doi":"10.1109/ECMR.2019.8870935","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870935","url":null,"abstract":"RGB-D cameras play an increasingly important role in localization and autonomous navigation of mobile robots. Reasonably priced commercial RGB-D cameras have recently been developed for operation in greenhouse and outdoor conditions. They can be employed for different agricultural and horticultural operations such as harvesting, weeding, pruning and phenotyping. However, the depth information extracted from the cameras varies significantly between objects and sensing conditions. This paper presents an evaluation protocol applied to a commercially available Fotonic F80 time-of-flight RGB-D camera for eight different object types. A case study of autonomous sweet pepper harvesting was used as an exemplary agricultural task. Each of the objects chosen is a possible item that an autonomous agricultural robot must detect and localize to perform well. A total of 340 rectangular regions of interests (ROI) were marked for the extraction of performance measures of point cloud density, and variability around center of mass, 30–100 ROIs per object type. An additional 570 ROIs were generated (57 manually and 513 replicated) to evaluate the repeatability and accuracy of the point cloud. A statistical analysis was performed to evaluate the significance of differences between object types. The results show that different objects have significantly different point density. Specifically metallic materials and black colored objects had significantly less point density compared to organic and other artificial materials introduced to the scene as expected. The point cloud variability measures showed no significant differences between object types, except for the metallic knife that presented significant outliers in collected measures. The accuracy and repeatability analysis showed that 1–3 cm errors are due to the the difficulty for a human to annotate the exact same area and up to ±4 cm error is due to the sensor not generating the exact same point cloud when sensing a fixed object.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130621830","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: 10

Indoor Scene Recognition based on Weighted Voting Schemes 基于加权投票方案的室内场景识别

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870931

A. C. Hernández, Clara Gómez, Erik Derner, R. Barber

{"title":"Indoor Scene Recognition based on Weighted Voting Schemes","authors":"A. C. Hernández, Clara Gómez, Erik Derner, R. Barber","doi":"10.1109/ECMR.2019.8870931","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870931","url":null,"abstract":"Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. “office”, “classroom” or “kitchen”) can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131376311","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

Lifelong Mapping using Adaptive Local Maps 使用自适应本地地图的终身映射

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870347

Nandan Banerjee, D. Lisin, Jimmy Briggs, Martín Llofriu, Mario E. Munich

引用次数: 8