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New vision sensors, such as the Dynamic and Active-pixel Vision sensor(DAVIS), incorporate a conventional global-shutter camera and an event-basedsensor in the same pixel array. These sensors have great potential forhigh-speed robotics and computer vision because they allow us to combine thebenefits of conventional cameras with those of event-based sensors: lowlatency, high temporal resolution, and very high dynamic range. However, newalgorithms are required to exploit the sensor characteristics and cope with itsunconventional output, which consists of a stream of asynchronous brightnesschanges (called "events") and synchronous grayscale frames. For this purpose,we present and release a collection of datasets captured with a DAVIS in avariety of synthetic and real environments, which we hope will motivateresearch on new algorithms for high-speed and high-dynamic-range robotics andcomputer-vision applications. In addition to global-shutter intensity imagesand asynchronous events, we provide inertial measurements and ground-truthcamera poses from a motion-capture system. The latter allows comparing the poseaccuracy of ego-motion estimation algorithms quantitatively. All the data arereleased both as standard text files and binary files (i.e., rosbag). Thispaper provides an overview of the available data and describes a simulator thatwe release open-source to create synthetic event-camera data.

The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM