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Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots

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Zenodo2025-05-20 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.11504582
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The dataset is password-protected until the article is accepted by the Scientific Data Journal. After acceptance, the dataset will be available to everyone. For any questions, comments or other issues please contact Piotr Woźniak, email: p.wozniak@prz.edu.pl. License The MDDRobots dataset is made available under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/. Summary The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions.  It is obligatory to cite the following paper in every work that uses the dataset:  Wozniak, P.; Krzeszowski T.; Kwolek B.: Multi-Domain Dataset for Indoor Place Recognition and Anomaly Detection by Mobile Robots, Scientific Data, ISSN: 2052-4463, 2024. Data description The data is divided into five sets, each containing data for different cameras, which have further subsets. Each subset (Training, Test 1, Test 2, and Test 3) consists of nine sequences. There are a total of 87,750 three-channel RGB color images in PNG format organized into 19 zip folders. Each image in the sequence is labeled to represent a room. The number of images for each subset differs due to the division into training and testing data, as well as different methods of recording the sequences. To ensure a balanced dataset, each room in the sequence has the same number of images. Different environmental changes are introduced in each subset, mainly due to changes in the route, robot, and recording equipment. The rooms are well-lit but not overexposed. Test 1 data are closest to those from the training set. Test 3 sequences present changed conditions, such as a different time of day, a changed lighting system, and intensive equipment changes. Test 2 sequences pose the most significant challenge as they contain various recorded activities performed by people moving around rooms. This sequence data does not appear in the Xtion subset. Dataset structure RobotPiCamera_DataSet DataSet_RobotPiCamera_RGB_train DataSet_RobotPiCamera_RGB_test1 DataSet_RobotPiCamera_RGB_test2 DataSet_RobotPiCamera_RGB_test3  Xtion_DataSet DataSet_XTION_RGB_train DataSet_XTION_RGB_test1 DataSet_XTION_RGB_test3  GOPRO_DataSet DataSet_GOPRO_RGB_train DataSet_GOPRO_RGB_test1 DataSet_GOPRO_RGB_test2 DataSet_GOPRO_RGB_test3 iPhone_DataSet DataSet_IPHONE_RGB_train DataSet_IPHONE_RGB_test1 DataSet_IPHONE_RGB_test2 DataSet_IPHONE_RGB_test3 P40PRO_DataSet DataSet_P40PRO_RGB_train DataSet_P40PRO_RGB_test1 DataSet_P40PRO_RGB_test2 DataSet_P40PRO_RGB_test3 Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png. Total Images (Images per Place) Subset Mounted Training Test 1 Test 2 Test 3 Pi Camera Robot 7200 (800) 5400 (600) 5400 (600) 5400 (600) Xtion Robot 7200 (800)  1800 (200)  - 1800 (200)  GoPro Hand 5400 (600) 4500 (500) 4500 (500) 4500 (500) iPhone Hand 5400 (600)  4500 (500) 4500 (500) 4500 (500)  P40Pro Hand 5400 (600) 4050 (450) 3150 (350)  3150 (350)  Further information For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.
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Zenodo
创建时间:
2024-08-21
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