MAgro数据集:用于农业环境中同步定位和制图的数据集

IF 7.5 1区 计算机科学 Q1 ROBOTICS International Journal of Robotics Research Pub Date : 2023-10-25 DOI:10.1177/02783649231210011
Mercedes Marzoa Tanco, Guillermo Trinidad Barnech, Federico Andrade, Javier Baliosian, Martin LLofriu, JM Di Martino, Gonzalo Tejera
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引用次数: 0

摘要

由于最近在计算机视觉和深度学习方面的创新,农业正在发生变革。然而,缺乏在自然农业环境中收集的具体数据集,可以说是新发现和基准的主要瓶颈。目前的工作提供了一个新的数据集,Magro和一个扩展数据收集的框架。我们介绍了第一个版本的Magro数据集V1.0,由9个ROS包(和相应的原始数据)组成,其中包含在苹果和梨作物中收集的数据。收集数据,在不同的日子、不同的光照和天气条件下重复固定的轨迹。为了支持闭环算法的评估,轨迹被设计成具有闭环,从不同的角度重新访问一些地方。我们使用Clearpath的Jackal机器人,该机器人配备了指向前方和左侧的立体摄像头、3D激光雷达、三个惯性测量单元(IMU)和轮式编码器。此外,我们提供校准的RTK GPS数据,可以用作地面真值。我们的数据集是公开的,它将被更新以拥有更多的数据和可变性。最后,我们在我们的新数据集上测试了两种现有的最先进的视觉和基于点云的定位和映射算法,以验证数据集的可用性。
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MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments
The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath’s Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset’s usability.
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来源期刊
International Journal of Robotics Research
International Journal of Robotics Research 工程技术-机器人学
CiteScore
22.20
自引率
0.00%
发文量
34
审稿时长
6-12 weeks
期刊介绍: The International Journal of Robotics Research (IJRR) has been a leading peer-reviewed publication in the field for over two decades. It holds the distinction of being the first scholarly journal dedicated to robotics research. IJRR presents cutting-edge and thought-provoking original research papers, articles, and reviews that delve into groundbreaking trends, technical advancements, and theoretical developments in robotics. Renowned scholars and practitioners contribute to its content, offering their expertise and insights. This journal covers a wide range of topics, going beyond narrow technical advancements to encompass various aspects of robotics. The primary aim of IJRR is to publish work that has lasting value for the scientific and technological advancement of the field. Only original, robust, and practical research that can serve as a foundation for further progress is considered for publication. The focus is on producing content that will remain valuable and relevant over time. In summary, IJRR stands as a prestigious publication that drives innovation and knowledge in robotics research.
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