DSEC: A Stereo Event Camera Dataset for Driving Scenarios

Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, operating conditions of current autonomous cars are mostly restricted to ideal scenarios. This means that driving in challenging illumination conditions such as night, sunrise, and sun...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 6; no. 3; pp. 4947 - 4954
Main Authors: Gehrig, Mathias, Aarents, Willem, Gehrig, Daniel, Scaramuzza, Davide
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Autonomous cars
Cameras
computer vision for transportation
data sets for robot learning
Data sets for robotic vision
Datasets
Dynamic range
Global Positioning System
High resolution
Illumination
Image color analysis
Lighting
Robot vision systems
Sunset
Temporal resolution
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, operating conditions of current autonomous cars are mostly restricted to ideal scenarios. This means that driving in challenging illumination conditions such as night, sunrise, and sunset remains an open problem. In these cases, standard cameras are being pushed to their limits in terms of low light and high dynamic range performance. To address these challenges, we propose, DSEC, a new dataset that contains such demanding illumination conditions and provides a rich set of sensory data. DSEC offers data from a wide-baseline stereo setup of two color frame cameras and two high-resolution monochrome event cameras. In addition, we collect lidar data and RTK GPS measurements, both hardware synchronized with all camera data. One of the distinctive features of this dataset is the inclusion of high-resolution event cameras. Event cameras have received increasing attention for their high temporal resolution and high dynamic range performance. However, due to their novelty, event camera datasets in driving scenarios are rare. This work presents the first high resolution, large scale stereo dataset with event cameras. The dataset contains 53 sequences collected by driving in a variety of illumination conditions and provides ground truth disparity for the development and evaluation of event-based stereo algorithms.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3068942