Aachen Day-Night v1.1 dataset is an extended version of the original Aachen Day-Night dataset. Besides the original query images, the Aachen Day-Night v1.1 dataset contains an additional 93 nighttime queries. In addition, it uses a larger 3D model containing additional images. These additional images were extracted from video sequences captured with different cameras. Please refer to Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis for more information.
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This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divi
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