2019 European Conference on Mobile Robots (ECMR)最新文献

Joint on-manifold self-calibration of odometry model and sensor extrinsics using pre-integration 基于预积分的里程计模型和传感器外部性的联合流形自校准

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-10-17 DOI: 10.1109/ECMR.2019.8870942

Jérémie Deray, J. Solà, J. Andrade-Cetto

{"title":"Joint on-manifold self-calibration of odometry model and sensor extrinsics using pre-integration","authors":"Jérémie Deray, J. Solà, J. Andrade-Cetto","doi":"10.1109/ECMR.2019.8870942","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870942","url":null,"abstract":"This paper describes a self-calibration procedure that jointly estimates the extrinsic parameters of an exteroceptive sensor able to observe ego-motion, and the intrinsic parameters of an odometry motion model, consisting of wheel radii and wheel separation. We use iterative nonlinear on-manifold optimization with a graphical representation of the state, and resort to an adaptation of the pre-integration theory, initially developed for the IMU motion sensor, to be applied to the differential drive motion model. For this, we describe the construction of a pre-integrated factor for the differential drive motion model, which includes the motion increment, its covariance, and a first-order approximation of its dependence with the calibration parameters. As the calibration parameters change at each solver iteration, this allows a posteriori factor correction without the need of re-integrating the motion data. We validate our proposal in simulations and on a real robot and show the convergence of the calibration towards the true values of the parameters. It is then tested online in simulation and is shown to accommodate to variations in the calibration parameters when the vehicle is subject to physical changes such as loading and unloading a freight.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125546649","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

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870962

Stephanie M. Lowry

引用次数: 2

Fiducial Marker based Extrinsic Camera Calibration for a Robot Benchmarking Platform 基于基准标记的机器人基准平台外部摄像机标定

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870969

Timo Korthals, Daniel Wolf, Daniel Rudolph, Marc Hesse, U. Rückert

引用次数: 1

Learning to see through haze: Radar-based Human Detection for Adverse Weather Conditions 学习透视雾霾:基于雷达的恶劣天气条件下的人类探测

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870954

Filip Majer, Zhi Yan, G. Broughton, Y. Ruichek, T. Krajník

引用次数: 20

Drone's Attitude Estimation in Corridor-Like Environments 走廊型环境下无人机姿态估计

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870961

D. Jano, S. Arogeti

引用次数: 1

Prioritized Multi-agent Path Finding for Differential Drive Robots 差分驱动机器人的优先多智能体寻径

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870957

K. Yakovlev, A. Andreychuk, Vitaly Vorobyev

{"title":"Prioritized Multi-agent Path Finding for Differential Drive Robots","authors":"K. Yakovlev, A. Andreychuk, Vitaly Vorobyev","doi":"10.1109/ECMR.2019.8870957","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870957","url":null,"abstract":"Methods for centralized planning of the collision-free trajectories for a fleet of mobile robots typically solve the discretized version of the problem and rely on numerous simplifying assumptions, e.g. moves of uniform duration, cardinal only translations, equal speed and size of the robots etc., thus the resultant plans can not always be directly executed by the real robotic systems. To mitigate this issue we suggest a set of modifications to the prominent prioritized planner - AA-SIPP(m) - aimed at lifting the most restrictive assumptions (syncronized translation only moves, equal size and speed of the robots) and at providing robustness to the solutions. We evaluate the suggested algorithm in simulation and on differential drive robots in typical lab environment (indoor polygon with external video-based navigation system). The results of the evaluation provide a clear evidence that the algorithm scales well to large number of robots (up to hundreds in simulation) and is able to produce solutions that are safely executed by the robots prone to imperfect trajectory following. The video of the experiments can be found at https://youtu.be/Fer_irn4BG0.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"30 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":"126787496","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: 18

Robot Exploration Using Knowledge of Inaccurate Floor Plans 利用不准确平面图知识的机器人探索

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870925

M. Luperto, Danilo Fusi, N. A. Borghese, F. Amigoni

引用次数: 6

Improving Navigation with the Social Force Model by Learning a Neural Network Controller in Pedestrian Crowds 用神经网络控制器学习社会力模型改进行人人群导航

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870923

P. Regier, Ibrahim Shareef, Maren Bennewitz

{"title":"Improving Navigation with the Social Force Model by Learning a Neural Network Controller in Pedestrian Crowds","authors":"P. Regier, Ibrahim Shareef, Maren Bennewitz","doi":"10.1109/ECMR.2019.8870923","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870923","url":null,"abstract":"In this paper, we present a novel, efficient approach to improve the acceleration commands computed by the popular social force model (SFM) [1] for navigation through pedestrian crowds. Our method consists of two stages. In the first phase, we collect training data with a simulated approach. In this step, we modify the steering acceleration commands from the SFM according to a set of discrete alterations and simulate the motion of the robot as well as the pedestrians into the future for each alteration. We rate each resulting trajectory based on a cost function and apply the best steering command to the robot. While controlling the robot in such way, we collect for every time step the input and output training data. In the second stage, we then learn a neural network given the collected training data. We use the best acceleration values experienced in the first phase as target values for the neural network and define simple input features describing the local surrounding of the robot. In extensive simulation experiments using different pedestrian densities, we demonstrate that the controls generated by the learned neural network lead to a significantly reduced number of collisions with pedestrians compared to the results of the basic SFM controller, while achieving similar or even shorter completion times.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"35 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":"134052851","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

Distributed Progressive Formation Control for Multi-Agent Systems: 2D and 3D deployment of UAVs in ROS/Gazebo with RotorS 多智能体系统的分布式渐进编队控制:无人机在带有旋翼的ROS/Gazebo中的二维和三维部署

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870934

Cassandra McCord, J. P. Queralta, Tuan Anh Nguyen Gia, Tomi Westerlund

{"title":"Distributed Progressive Formation Control for Multi-Agent Systems: 2D and 3D deployment of UAVs in ROS/Gazebo with RotorS","authors":"Cassandra McCord, J. P. Queralta, Tuan Anh Nguyen Gia, Tomi Westerlund","doi":"10.1109/ECMR.2019.8870934","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870934","url":null,"abstract":"Coordination of multiple robots in order to cooperatively perform a given task requires a certain distribution of the different units in space. Furthermore, individual robots, or agents, might have different tasks, or positions, assigned. Formation control algorithms might rely on a priori information, a centralized controller, or communication among the agents to assign roles. Distributed approaches that only need local interaction between agents have limited possibilities, such as flocks where agents actively control the distance to neighboring agents. Alternatively, two-way local communication has been applied to progressively assign roles and converge towards a given configuration. We propose a progressive assignment algorithm and formation control scheme that extends leader-follower formations in order to enable cooperation of multiple robots with minimal, one-way, local communication between agents. The proposed algorithm progressively generates a directed, locally convex, path graph to uniquely assign formation positions to all agents. The low computational complexity of our algorithm enables its implementation in resource-limited devices. Agents only require information about neighboring agents and be able to locally broadcast their status. This algorithm enables almost arbitrary two-dimensional configurations, with the only limitation being the sensing range enabling the definition of a series of convex hulls in a certain subset of agents such that agents sharing an edge in the hull are able to sense each other. Moreover, we propose a methodology for deploying agents to an arbitrary three-dimensional configuration after the assignment process is made on the plane.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"32 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":"115615217","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: 22

Semantic Localization through Propagation of Scene Information in a Hierarchical Model 层次模型中基于场景信息传播的语义定位

2019 European Conference on Mobile Robots (ECMR) Pub Date : 2019-09-01 DOI: 10.1109/ECMR.2019.8870972

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

{"title":"Semantic Localization through Propagation of Scene Information in a Hierarchical Model","authors":"Clara Gómez, A. C. Hernández, Erik Derner, R. Barber","doi":"10.1109/ECMR.2019.8870972","DOIUrl":"https://doi.org/10.1109/ECMR.2019.8870972","url":null,"abstract":"The success of mobile robots, and particularly these coexisting with humans, relies on the ability to understand human environments. Representing the world and analysing spaces in a similar way to humans will enhance their comprehension and enable higher abstraction capabilities and interactions. The purpose of this work is to develop a localization framework that takes into account the different scenes common in a human environment and a hierarchical model of the environment. A probabilistic model for recognizing scenes is employed to determine the scene in which the robot is located. To allow that, the information about the objects and the relationships between them are considered. Besides that, a hierarchical model formed by different topological representations according to different levels of abstraction is proposed. Localization is performed at different levels to improve the localization accuracy. In this work, scene information is used to improve the localization of a mobile robot in a hierarchical model using hidden Markov models. The experiments of our framework working in real environments uphold the usefulness of the inclusion of the understanding and abstraction of the environment in localization.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"339 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":"114728741","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