  We present a challenging dataset, the TartanAir, for robot navigation tasks
and more. The data is collected in photo-realistic simulation environments with
the presence of moving objects, changing light and various weather conditions.
By collecting data in simulations, we are able to obtain multi-modal sensor
data and precise ground truth labels such as the stereo RGB image, depth image,
segmentation, optical flow, camera poses, and LiDAR point cloud. We set up
large numbers of environments with various styles and scenes, covering
challenging viewpoints and diverse motion patterns that are difficult to
achieve by using physical data collection platforms. In order to enable data
collection at such a large scale, we develop an automatic pipeline, including
mapping, trajectory sampling, data processing, and data verification. We
evaluate the impact of various factors on visual SLAM algorithms using our
data. The results of state-of-the-art algorithms reveal that the visual SLAM
problem is far from solved. Methods that show good performance on established
datasets such as KITTI do not perform well in more difficult scenarios.
Although we use the simulation, our goal is to push the limits of Visual SLAM
algorithms in the real world by providing a challenging benchmark for testing
new methods, while also using a large diverse training data for learning-based
methods. Our dataset is available at
\url{http://theairlab.org/tartanair-dataset}.
