TartanAir: A Dataset to Push the Limits of Visual SLAM

Wenshan Wang,Delong Zhu,Xiangwei Wang,Yaoyu Hu,Yuheng Qiu,Chen Wang,Yafei Hu,Ashish Kapoor,Sebastian Scherer

IROS（2020）

引用 198|浏览122

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摘要

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.

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关键词

challenging dataset,robot navigation tasks,photo-realistic simulation environments,moving objects,changing light,weather conditions,multimodal sensor data,precise ground truth labels,stereo RGB image,depth image,optical flow,LiDAR point cloud,challenging viewpoints,diverse motion patterns,physical data collection platforms,automatic pipeline,data processing,data verification,visual SLAM algorithms,visual SLAM problem,established datasets,difficult scenarios,Visual SLAM algorithms,challenging benchmark,diverse training data

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