2021 European Conference on Mobile Robots (ECMR)最新文献_第4页

Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning 基于强化学习的信息路径规划策略自适应选择

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568796

Taeyeong Choi, Grzegorz Cielniak

{"title":"Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning","authors":"Taeyeong Choi, Grzegorz Cielniak","doi":"10.1109/ecmr50962.2021.9568796","DOIUrl":"https://doi.org/10.1109/ecmr50962.2021.9568796","url":null,"abstract":"In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as \"attraction force\" to deployed robots in path planning. Although the integration with Traveling Salesman Problem (TSP) solvers was also shown to produce relatively short travel distance, we here hypothesise several factors that could decrease the overall prediction precision as well because sub-optimal locations may eventually be included in their paths. To address this issue, in this paper, we first explore \"local planning\" approaches adopting various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level controllers are trained to adaptively produce blended plans from a particular set of local planners to inherit unique strengths from that selection depending on latest prediction states. Our experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans that a single planner could not create alone but also ensure significantly reduced travel distances at no cost of prediction reliability without any assist of additional modules for shortest path calculation.","PeriodicalId":200521,"journal":{"name":"2021 European Conference on Mobile Robots (ECMR)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123478322","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

3D Road Boundary Estimation using 3D LiDAR with Scanline-wise 1D Deep Feature and Particle Filtering 基于扫描线一维深度特征和粒子滤波的三维激光雷达道路边界估计

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568784

Yuta Nakayama, J. Miura

引用次数: 2

A Flexible Framework for Virtual Omnidirectional Vision to Improve Operator Situation Awareness 一种提高操作者态势感知的虚拟全方位视觉灵活框架

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ECMR50962.2021.9568840

M. Oehler, O. Stryk

引用次数: 4

Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy 重新校准KITTI数据集相机设置以提高里程计精度

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568821

Igor Cvisic, Ivan Marković, Ivan Petrović

{"title":"Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy","authors":"Igor Cvisic, Ivan Marković, Ivan Petrović","doi":"10.1109/ecmr50962.2021.9568821","DOIUrl":"https://doi.org/10.1109/ecmr50962.2021.9568821","url":null,"abstract":"Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to bench-mark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the pro-posed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.","PeriodicalId":200521,"journal":{"name":"2021 European Conference on Mobile Robots (ECMR)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122997921","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: 13

Learning to plan people-aware trajectories for robot navigation: A genetic algorithm* 学习规划机器人导航的人感知轨迹:一种遗传算法*

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568804

Alberto Bacchin, Gloria Beraldo, E. Menegatti

引用次数: 1

Let Your Robot Go – Challenges of a Decentralized Remote Robot Competition 让你的机器人走-分散远程机器人竞赛的挑战

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568845

Tim Laue, Arne Moos, Patrick Göttsch

引用次数: 3

Robust collaborative collision avoidance between robots with nearly symmetric crossing trajectories 具有近对称交叉轨迹的机器人之间的鲁棒协同避碰

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568823

Grimaldo Silva, K. Rekik, J. Crowley

引用次数: 0

A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving 一种用于自动驾驶的混合稀疏-密集单目SLAM系统

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568797

Louis Gallagher, V. Kumar, S. Yogamani, J. McDonald

{"title":"A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving","authors":"Louis Gallagher, V. Kumar, S. Yogamani, J. McDonald","doi":"10.1109/ecmr50962.2021.9568797","DOIUrl":"https://doi.org/10.1109/ecmr50962.2021.9568797","url":null,"abstract":"In this paper, we present a system for incrementally reconstructing a dense 3D model of the geometry of an outdoor environment using a single monocular camera attached to a moving vehicle. Dense models provide a rich representation of the environment facilitating higher-level scene understanding, perception, and planning. Our system employs dense depth prediction with a hybrid mapping architecture combining state-of-the-art sparse features and dense fusion-based visual SLAM algorithms within an integrated framework. Our novel contributions include design of hybrid sparse-dense camera tracking and loop closure, and scale estimation improvements in dense depth prediction. We use the motion estimates from the sparse method to overcome the large and variable inter-frame displacement typical of outdoor vehicle scenarios. Our system then registers the live image with the dense model using whole-image alignment. This enables the fusion of the live frame and dense depth prediction into the model. Global consistency and alignment between the sparse and dense models are achieved by applying pose constraints from the sparse method directly within the deformation of the dense model. We provide qualitative and quantitative results for both trajectory estimation and surface reconstruction accuracy, demonstrating competitive performance on the KITTI dataset. Qualitative results of the proposed approach are illustrated in https://youtu.be/Pn2uaVqjskY. Source code for the project is publicly available at the following repository https://github.com/robotvisionmu/DenseMonoSLAM","PeriodicalId":200521,"journal":{"name":"2021 European Conference on Mobile Robots (ECMR)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115794792","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: 12

A blind-spot-aware optimization-based planner for safe robot navigation 基于盲点感知优化的机器人安全导航规划器

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568820

Kenny Schlegel, Peter Weissig, P. Protzel

{"title":"A blind-spot-aware optimization-based planner for safe robot navigation","authors":"Kenny Schlegel, Peter Weissig, P. Protzel","doi":"10.1109/ecmr50962.2021.9568820","DOIUrl":"https://doi.org/10.1109/ecmr50962.2021.9568820","url":null,"abstract":"Safe mobile robot navigation should consider not only collision avoidance with current obstacles but also include non-visible areas (to which we refer as blind spots) and the resulting risk of collision with hidden moving objects (e.g. people). Such capability is important for mobile robots operating in environments shared with humans - for instance a shopping assistant robot in a supermarket. This work aims to extend an existing motion planner for mobile robots (the Time Elastic Band planner) by including blind spots. As a result, the final planner does not only consider static and visible dynamic obstacles, but handles blind spots, too. To identify such blind spots, we define and use critical corners that imply them. Hence, our contributions in this paper are creating a critical corner detector, which operates on laser scan data, and the extension of a factor-graph-based path planner. We evaluate the proposed method standalone and in our simulation environment of a supermarket. It can be seen that the implementation is capable of detecting and dealing with blind spots. Finally, we provide source code for both the detector and the planner extensions.","PeriodicalId":200521,"journal":{"name":"2021 European Conference on Mobile Robots (ECMR)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115197716","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

Robust Visual Teach and Repeat Navigation for Unmanned Aerial Vehicles 无人机的鲁棒视觉教学和重复导航

2021 European Conference on Mobile Robots (ECMR) Pub Date : 2021-08-01 DOI: 10.1109/ecmr50962.2021.9568807

V. Kozák, Tomás Pivonka, Pavlos Avgoustinakis, Lukás Majer, Miroslav Kulich, L. Preucil, Luis G. Camara

{"title":"Robust Visual Teach and Repeat Navigation for Unmanned Aerial Vehicles","authors":"V. Kozák, Tomás Pivonka, Pavlos Avgoustinakis, Lukás Majer, Miroslav Kulich, L. Preucil, Luis G. Camara","doi":"10.1109/ecmr50962.2021.9568807","DOIUrl":"https://doi.org/10.1109/ecmr50962.2021.9568807","url":null,"abstract":"Vision-based navigation is one of the leading tasks in mobile robotics. It, however, introduces additional challenges in long-term autonomy due to its reliance on stable visual features. As such, visual navigation methods are often sensitive to appearance changes and unreliable in environments with low feature density. We present a teach-and-repeat navigation system for unmanned aerial vehicles (UAVs) equipped with a low-end camera. We use a novel visual place recognition methodology based on high-level CNN features to localize a robot on a previously traversed trajectory and to directly calculate heading corrections for navigation. The developed navigation method is fully vision-based and independent of other sensory information, making it universal and easily transferable. The system has been experimentally verified and evaluated with respect to a state-of-the-art ORB2-TaR navigation system. It showed comparable results in terms of its precision and robustness to environmental changes. In addition, the system was able to safely navigate in environments with low feature density and to reliably solve the wake-up robot problem.","PeriodicalId":200521,"journal":{"name":"2021 European Conference on Mobile Robots (ECMR)","volume":"335 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124304710","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