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

Are we ready for beyond-application high-volume data? The Reeds robot perception benchmark dataset