TartanAir: A Dataset to Push the Limits of Visual SLAM - Wang, Wenshan; Zhu, Delong; Wang, Xiangwei; Hu, Yaoyu; Qiu, Yuheng; Wang, Chen; Hu, Yafei; Kapoor, Ashish; Scherer, Sebastian (ArXiv, 2020)
We present a challenging dataset, the TartanAir, for robot navigation task and more. The data is collected in photo-realistic simulation environments in the presence of various light conditions, weather and moving objects. By collecting data in simulation, we are able to obtain multi-modal sensor data and precise ground truth labels, including the stereo RGB image, depth image, segmentation, optical flow, camera poses, and LiDAR point cloud. We set up a large number of environments with various styles and scenes, covering challenging viewpoints and diverse motion patterns, which are difficult to achieve by using physical data collection platforms.