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2 results for “Visual Place Recognition”
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Only Look Once, Mining Distinctive Landmarks from ConvNet for Visual Place Recognition
<p>This video illustrates the content of the paper referenced below.</p> <p><strong> Reference:</strong><br> Zetao Chen, Fabiola Maffra, Inkyu Sa and Margarita Chli, "Only look once, mining distinctive landmarks from ConvNet for visual place recognition", in Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2017.</p> <p><strong>Abstract:</strong><br> Recently, image representations derived from Convolutional Neural Networks (CNNs) have been demonstrated to achieve impressive performance on a wide variety of tasks, including place recognition. In this paper, we take a step deeper into the internal structure of CNNs and propose novel CNN-based image features for place recognition by identifying salient regions and creating their regional representations directly from the convolutional layer activations. A range of experiments is conducted on challenging datasets with varied conditions and viewpoints. These reveal superior precision-recall characteristics and robustness against both viewpoint and appearance variations for the proposed approach over the state of the art. By analyzing the feature encoding process of our approach, we provide insights into what makes an image presentation robust against external variations.</p>
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