title: A Dataset for Visual Navigation with Neuromorphic Methods.
abstract: 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.
authors.affiliation: Research Center on Information and Communication Technologies (CITIC), Department of Computer Architecture and Computer Technology, University of GranadaGranada, Spain; Computer Vision Lab, University of Maryland Institute for Advanced Computer Studies (UMIACS), Department of Computer Science, University of MarylandCollege Park, MD, USA.
authors.forename: Francisco
authors.initials: F
authors.lastname: Barranco
authors.affiliation: Computer Vision Lab, University of Maryland Institute for Advanced Computer Studies (UMIACS), Department of Computer Science, University of Maryland College Park, MD, USA.
authors.forename: Cornelia
authors.initials: C
authors.lastname: Fermuller
authors.affiliation: Computer Vision Lab, University of Maryland Institute for Advanced Computer Studies (UMIACS), Department of Computer Science, University of Maryland College Park, MD, USA.
authors.forename: Yiannis
authors.initials: Y
authors.lastname: Aloimonos
authors.affiliation: Department of Information Technology and Electrical Engineering, Institute of Neuroinformatics, ETH Zurich and University of Zurich Zurich, Switzerland.
authors.forename: Tobi
authors.initials: T
authors.lastname: Delbruck
keywords: calibration; dataset; event-driven methods; frame-free sensors; visual navigation