Supported Datasets
The EuRoC MAV Dataset
The ETH ASL EuRoC MAV dataset [1] is one of the most used datasets in the visual-inertial / simultaneous localization and mapping (SLAM) research literature. The reason for this is the synchronised inertial+camera sensor data and the high quality groundtruth. The dataset contains different sequences of varying difficulty of a Micro Aerial Vehicle (MAV) flying in an indoor room. Monochrome stereo images are collected by a two Aptina MT9V034 global shutter cameras at 20 frames per seconds, while a ADIS16448 MEMS inertial unit provides linear accelerations and angular velocities at a rate of 200 samples per second. We recommend that most users start testing on this dataset before moving on to the other datasets that our system support or before trying with your own collected data. Note that we also focus on the vicon room datasets as full 6 dof pose collection of the MAV was available.
TUM Visual-Inertial Dataset
The TUM Visual-Inertial Dataset [13] is a more recent dataset that was presented to provide a way to evaluate state-of-the-art visual inertial odometery approaches. As compared to the EuRoC MAV datasets, this dataset provides photometric calibration of the cameras which has not been available in any other visual-inertal dataset for researchers. Monochrome stereo images are collected by two IDS uEye UI-3241LE-M-GL global shutter cameras at 20 frames per second, while a Bosch BMI160 inertial unit provides linear accelerations and angular velocities at a rate of 200 samples per second. Not all datasets have groundtruth avalible throughout the entire trajectory as the motion capture system is limited to the starting and ending room. There are quite a few very challenging outdoor handheld datasets which are a challenging direction for research. Note that we focus on the room datasets as full 6 dof pose collection is available over the total trajectory.
RPNG Dataset
In additional the above community maintained datasets, we have also released a few datasets. Please cite the Open VINS paper if you use any of these datasets in your works Most of these datasets do not have perfect calibration parameters, and some are not time synchronised. Thus, please ensure that you have enabled online calibration of these parameters.