DeepLoc is a large-scale urban outdoor localization dataset. The dataset is currently comprised of one scene spanning an area of 110 x 130 m, that a robot traverses multiple times with different driving patterns. The dataset creators use a LiDAR-based SLAM system with sub-centimeter and sub-degree accuracy to compute the pose labels that provided as groundtruth. Poses in the dataset are approximately spaced by 0.5 m which is twice as dense as other relocalization datasets.
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A Large Dataset of Object Scans is a dataset of more than ten thousand 3D scans of real objects. To create the dataset, the authors recruited 70 operators, equipped them with consumer-grade mobile 3D scanning setups, and paid them to scan objects in their environments. The operators scanned objects of their choosing, outside the laboratory and without direct supervision by computer vision professionals. The result is a large and diverse collection of object scans: from shoes, mugs, and toys to grand pianos, construction vehicles, and large outdoor sculptures. The authors worked with an attorney to ensure that data acquisition did not violate privacy constraints. The acquired data was placed in the public domain and is available freely.
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DeepLocCross is a localization dataset that contains RGB-D stereo images captured at 1280 x 720 pixels at a rate of 20 Hz. The ground-truth pose labels are generated using a LiDAR-based SLAM system. In addition to the 6-DoF localization poses of the robot, the dataset additionally contains tracked detections of the observable dynamic objects. Each tracked object is identified using a unique track ID, spatial coordinates, velocity and orientation angle. Furthermore, as the dataset contains multiple pedestrian crossings, labels at each intersection indicating its safety for crossing are provided. This dataset consists of seven training sequences with a total of 2264 images, and three testing sequences with a total of 930 images. The dynamic nature of the surrounding environment at which the dataset was captured renders the tasks of localization and visual odometry estimation extremely challenging due to the varying weather conditions, presence of shadows and motion blur caused by the mov
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