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.
Furthermore, for each image the dataset creators provide pixel-wise semantic segmentation annotations for ten categories: Background, Sky, Road, Sidewalk, Grass, Vegetation, Building, Poles & Fences, Dynamic and Void. The dataset is divided into a train and test splits such that the train set comprises seven loops with alternating driving styles amounting to 2737 images, while the test set comprises three loops with a total of 1173 images. The dataset also contains global GPS/INS data and LiDAR measurements.
This dataset can be very challenging for vision based applications such as global localization, camera relocalization, semantic segmentation, visual odometry and loop closure detection, as it contains substantial lighting, weather changes, repeating structures, reflective and transparent glass buildings.Source: http://deeploc.cs.uni-freiburg.de/