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A Dataset for Visual Navigation with Neuromorphic Methods

机译:使用神经形态方法进行视觉导航的数据集

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摘要

Standardized benchmarks in Computer Vision have greatly contributed to the advance of approaches to many problems in the field. If we want to enhance the visibility of event-driven vision and increase its impact, we will need benchmarks that allow comparison among different neuromorphic methods as well as comparison to Computer Vision conventional approaches. We present datasets to evaluate the accuracy of frame-free and frame-based approaches for tasks of visual navigation. Similar to conventional Computer Vision datasets, we provide synthetic and real scenes, with the synthetic data created with graphics packages, and the real data recorded using a mobile robotic platform carrying a dynamic and active pixel vision sensor (DAVIS) and an RGB+Depth sensor. For both datasets the cameras move with a rigid motion in a static scene, and the data includes the images, events, optic flow, 3D camera motion, and the depth of the scene, along with calibration procedures. Finally, we also provide simulated event data generated synthetically from well-known frame-based optical flow datasets.
机译:Computer Vision中的标准化基准对推动解决该领域许多问题的方法做出了巨大贡献。如果我们想增强事件驱动视觉的可见性并增加其影响,我们将需要基准,以允许在不同的神经形态方法之间进行比较以及与计算机视觉常规方法进行比较。我们提出了数据集,以评估视觉导航任务的无框架和基于框架的方法的准确性。与传统的计算机视觉数据集相似,我们提供合成和真实场景,并通过图形包创建合成数据,并使用带有动态和有源像素视觉传感器(DAVIS)和RGB +深度传感器的移动机器人平台记录真实数据。对于这两个数据集,摄像机都在静态场景中以刚性运动移动,并且数据包括图像,事件,光流,3D摄像机运动和场景深度以及校准过程。最后,我们还提供了从知名的基于帧的光流数据集综合生成的模拟事件数据。

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