DOI: 10.5281/zenodo.1168074
title: A Dataset For Robotic Outdoor Visual Navigation With Multiple Passages Through Trajectory Segments

description: The images were captured by a fisheye camera and a magnetic compass was used to acquire the orientation data. The datasets are split in two folders:<br>
1) LEARN: In order to learn a new place, the robot camera captures 15 images over a 360 degrees panorama. During this process, the robot stays still in order to avoid distortions in the representation of the place.<br>
2) EXPLO: When exploring the environment (i.e. the rest of the time), the robot only captures 7 images per panorama, for the purpose of faster place recognition. Images are captured while the robot is moving. Various exploration panoramas are recorded around the trajectory performed in the learning panoramas (see traj.pdf).<br>
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The average distance between two learning panoramas is 0.93 +/- 0.03 meters<br>
The average distance traveled during an exploration panoramas is 0.71 +/- 0.01 meters<br>
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DATASET A<br>
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- 20 meters long<br>
- 22 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br>
- 5 exploration trajectories<br>
- A_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br>
- A_parallel: 29 exploration panoramas<br>
- A_diagonal1: 28 exploration panoramas<br>
- A_diagonal2: 30 exploration panoramas<br>
- A_diagonal3: 29 exploration panoramas<br>
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DATASET B<br>
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- 20 meters long<br>
- 21 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br>
- 4 exploration trajectories<br>
- B_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br>
- B_parallel: 29 exploration panoramas<br>
- B_diagonal1: 29 exploration panoramas<br>
- B_diagonal2: 29 exploration panoramas<br>
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DATASET C<br>
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- 23.1 meters long<br>
- 25 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br>
- 2 exploration trajectories<br>
- C_on_learned: 34 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br>
- C_parallel: 34 exploration panoramas<br>
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PANO_INFO FILE STRUCTURE<br>
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Every folder containing images also contains an info file, named either learn_pano_info.SAVE or explo_pano_info.SAVE. Each line corresponds to an image. The structures is the following:<br>
- column 1: id = image_id + 1<br>
- column 2: azimuth of the center of the image in degrees/360 (value in [0,1])<br>
- column 3: elevation of the center of the image. irrelevant in this database (equal to 0).<br>
- column 4: type of panorama: equal to 1 if learning and to 0 if exploration.<br>
- column 5: end of panorama: equal to 1 if it corresponds to the last image of a panorama.<br>
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REFERENCES<br>
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The dataset was used in the paper: Belkaid, M., Cuperlier, N., and Gaussier, P. Combining local and global visual information in context-based neurorobotic navigation. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 4947-4954, doi: 10.1109/IJCNN.2016.7727851, 2016.<br>
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description: {"references": ["Belkaid et al. (2016), Combining local and global visual information in context-based neurorobotic navigation. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 4947-4954, doi: 10.1109/IJCNN.2016.7727851"]}