Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 6, Issue 3, Pages 4947-4954Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3068942
Keywords
Data sets for robotic vision; data sets for robot learning; computer vision for transportation
Categories
Funding
- Swiss National Science Foundation (SNSF) through the National Center of Competence in Research (NCCR) Robotics
- SNSF-ERC Starting Grant
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Autonomous driving has received significant corporate funding in the past decade, but operating in challenging illumination conditions remains an open problem. To address this issue, a new dataset called DSEC, containing high-resolution event cameras, is proposed to provide rich sensory data for development and evaluation of event-based stereo algorithms.
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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