Insights into Batch Selection for Event-camera Motion Estimation - Datasets

See https://github.com/event-driven-robotics/batch-selection-experiments for examples on how to read and process the data.

Simulated datasets were used to accurately have a ground-truth of 3 DoF camera rotation, for valid training and evaluation. Four photo-realistic simulated environments from UnrealCV Zoo were selected as they are publicly available indoor scenes and include diverse lighting textures, shadows, reflections and object clutter. The camera was positioned inside the virtual room and rotated along all its three axes randomly at a variety of speeds. Frames were generated at >1 kHz from which the event-stream is generated using log-image-difference  techniques. Five velocity trajectories were used for arch1, four velocity trajectories were used for arch2, and three trajectories for arch3. Including the final room dataset, a total of 13 different datasets (each with a different velocity trajectory) cover a total of 190 seconds of data. Note: room is split into 3 archived files.

Please cite:

Valerdi, J.L., Bartolozzi, C. and Glover, A., 2023. Insights into Batch Selection for Event-Camera Motion Estimation. Sensors, 23(7), p.3699.

@article{valerdi2023insights,
title={Insights into Batch Selection for Event-Camera Motion Estimation},
author={Valerdi, Juan L and Bartolozzi, Chiara and Glover, Arren},
journal={Sensors},
volume={23},
number={7},
pages={3699},
year={2023},
publisher={MDPI}
}