This paper presents a dataset, called Reeds, for research on robot perception algorithms. The dataset aims to provide demanding benchmark opportunities for algorithms, rather than providing an environment for testing application-specific solutions. A boat was selected as a logging platform in order to provide highly dynamic kinematics. The sensor package includes six high-performance vision sensors, two long-range lidars, radar, as well as GNSS and an IMU. The spatiotemporal resolution of sensors were maximized in order to provide large variations and flexibility in the data, offering evaluation at a large number of different resolution presets based on the resolution found in other datasets. Reeds also provides means of a fair and reproducible comparison of algorithms, by running all evaluations on a common server backend. As the dataset contains massive-scale data, the evaluation principle also serves as a way to avoid moving data unnecessarily. It was also found that naive evaluation of algorithms, where each evaluation is computed sequentially, was not practical as the fetch and decode task of each frame would not scale well. Instead, each frame is only decoded once and then fed to all algorithms in parallel, including for GPU-based algorithms.

翻译：本文为机器人感知算法的研究提供了一个数据集,称为Reeds。 数据集的目的是为算法提供要求很高的基准机会,而不是为测试具体应用的解决方案提供一个环境。 选择了一艘船作为记录平台,以提供高度动态的动态运动学。 传感器包包括6个高性能视觉传感器、2个远程激光传感器、雷达以及全球导航卫星系统和一个IMU。 传感器的随机时空分辨率最大化,以便提供数据的巨大变异性和灵活性, 根据其他数据集中找到的分辨率提供大量不同分辨率预设的评价。 Reeds还提供公平、可复制的算法比较手段, 在通用服务器后端运行所有评价。 由于数据集包含大规模数据,评价原则也是一种避免不必要移动数据的方法。 还发现,对每次评价都是按顺序计算的算法的天真性评估并不切实际,因为每个框架的取取和解码任务不会大尺度化。 相反,每个框架都只对算法进行一次解码,然后将所有平行的GPLA进行配置。