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 movement of the robot platform. Furthermore, the presence of multiple dynamic objects often results in partial and full occlusions to the informative regions of the image. Moreover, the presence of repeated structures render the pose estimation task more challenging. Overall this dataset covers a wide range of perception related tasks such as loop closure detection, semantic segmentation, visual odometry estimation, global localization, scene flow estimation and behavior prediction.

Source: http://deeploc.cs.uni-freiburg.de/

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