KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odome
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Mapillary Vistas Dataset is a diverse street-level imagery dataset with pixel‑accurate and instance‑specific human annotations for understanding street scenes around the world.
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The NCLT dataset is a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan’s North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The dataset was collected to facilitate research focusing on long-term autonomous operation in changing environments. The dataset is comprised of 27 sessions spaced approximately biweekly over the course of 15 months. The sessions repeatedly explore the campus, both indoors and outdoors, on varying trajectories, and at different times of the day across all four seasons. This allows the dataset to capture many challenging elements including: moving obstacles (e.g., pedestrians, bicyclists, and cars), changing lighting, varying viewpoint, seasonal and weather changes (e.g., falling leaves and snow), and long-term structural changes caused by construction projects.
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DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface.
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The Retrieval-SFM dataset is used for instance image retrieval. The dataset contains 28559 images from 713 locations in the world. Each image has a label indicating the location it belongs to. Most locations are famous man-made architectures such as palaces and towers, which are relatively static and positively contribute to visual place recognition. The training dataset contains various perceptual changes including variations in viewing angles, occlusions and illumination conditions, etc.
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FAS100K is a large-scale visual localization dataset. This dataset is comprised of two traverses of 238 and 130 kms respectively where the latter is a partial repeat of the former. The data was collected using stereo cameras in Australia under sunny day conditions. It covers a variety of road and environment types including urban and rural areas. The raw image data from one of the cameras streaming at 5 Hz constitutes 63,650 and 34,497 image frames for the two traverses respectively.
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The NAVER LABS localization datasets are 5 new indoor datasets for visual localization in challenging real-world environments. They were captured in a large shopping mall and a large metro station in Seoul, South Korea, using a dedicated mapping platform consisting of 10 cameras and 2 laser scanners. In order to obtain accurate ground truth camera poses, we used a robust LiDAR SLAM which provides initial poses that are then refined using a novel structure-from-motion based optimization. The datasets are provided in the kapture format and contain about 130k images as well as 6DoF camera poses for training and validation. We also provide sparse Lidar-based depth maps for the training images. The poses of the test set are withheld to not bias the benchmark.
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