The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. (February 2017)

• Record Type: Journal Article
• Authors: Mueggler, Elias; Rebecq, Henri; Gallego, Guillermo; Delbruck, Tobi; Scaramuzza, Davide
• Is Part Of: International journal of robotics research. Volume 36:Number 2(2017)
• Page Start: 142
• Page End: 149
• Publication Date: 2017-02
• Subjects: Event-based cameras -- visual odometry -- SLAM -- simulation; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364917691115
• Languages: English
• ISSNs: 0278-3649

Abstract

New vision sensors, such as the dynamic and active-pixel vision sensor (DAVIS), incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array. These sensors have great potential for high-speed robotics and computer vision because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and very high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called "events") and synchronous grayscale frames. For this purpose, we present and release a collection of datasets captured with a DAVIS in a variety of synthetic and real environments, which we hope will motivate research on new algorithms for high-speed and high-dynamic-range robotics and computer-vision applications. In addition to global-shutter intensity images and asynchronous events, we provide inertial measurements and ground-truth camera poses from a motion-capture system. The latter allows comparing the pose accuracy of ego-motion estimation algorithms quantitatively. All the data are released both as standard text files and binary files (i.e. rosbag). This paper provides an overview of the available data and describes a simulator that we release open-source to create synthetic event-camera data.