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OpenLORIS-2020
Classification
Robot
|...
License: BSD-4-Clause

Overview

We provide a new lifelong robotic vision dataset (“OpenLORIS-Object”) collected via RGB-D cameras
mounted on mobile robots. The dataset embeds the challenges faced by a robot in the real-life
application and provides new benchmarks for validating lifelong object recognition algorithms.

Data Collection

Several grounded robots mounted by depth cameras and
other sensors are used for the data collection. These robots are
moving in the offices, homes, and malls, where the scenes are
diverse and changing all the time. In the OpenLORIS-Object
dataset, we provide the RGB-D video dataset for the objects.

The robot is actively recording the videos of targeted objects under multiple illuminations,
occlusions, camera-object distances/angles, and context information (clutters). We do include
the common challenges that the robot is usually faced with. For example,

  • Illumination. In
    a real-world application, the illumination can vary significantly across time, e.g., day and
    night differences. We repeat the data collection under weak, normal, and strong lighting conditions,
    respectively. The task becomes challenging with lights to be very weak.
  • Occlusion. Occlusion
    happens when a part of an object is hidden by other objects, or only a portion of the object
    is visible in the field of view. Since distinctive characteristics of the object might be hidden,
    occlusion significantly increases the difficulty for recognition.
  • Object size. Small-size
    or elongated objects make the task challenging, like dry batteries or glue sticks.
  • Camera-object
    angles/distances. The angles of the cameras affect the attributes detected from the object.
  • Clutter. Clutter refers to the presence of other objects in the vicinity of the considered
    object. The simultaneous presence of multiple objects may interfere with the classification
    task.

Data Format

The levels 1, 2, and 3 are ranked with increasing difficulties. For each instance at each level,
we provided 260 to 600 samples, both have RGB and depth images. Thus, the total images provided
is around 2 (RGB and depth) × 381 (mean samples per instance)× 121 (instances) × 4 (factors
per level) × 3 (difficulty levels) = 1, 106, 424 images. Also, we have provided bounding boxes
and masks for each RGB image. An example of two RGB-D frames of simple and complex clutter
with 2D bounding box and mask annotations is shown in Fig. 2. The size of images under illumination,
occlusion and clutter factors is 424×240 pixels, and the size of images under object pixel
size factor are 424×240, 320×180, 1280×720 pixels.

Citation

The data provided here is the 1st version for evaluating Lifelong Object Recognition algorithms.
Please cite our paper below in any academic work done with this dataset.

Qi She et al. "OpenLORIS-Object:
A Dataset and Benchmark towards Lifelong Object Recognition". arXiv:1911.06487, 2019
Qi She
et al., "IROS 2019 Lifelong Robotic Vision: Object Recognition Challenge [Competitions]," in
IEEE Robotics & Automation Magazine, vol. 27, no. 2, pp. 11-16, June 2020, doi: 10.1109/MRA.2020.2987186.


@inproceedings{she2019openlorisobject,
    title={ {OpenLORIS-Object}: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning},
    author={Qi She and Fan Feng and Xinyue Hao and Qihan Yang and Chuanlin Lan and Vincenzo
Lomonaco and Xuesong Shi and Zhengwei Wang and Yao Guo and Yimin Zhang and Fei Qiao and Rosa
H. M. Chan},
    booktitle={2020 International Conference on Robotics and Automation (ICRA)},
    year={2020},
    pages={4767-4773},
}
}


@article{9113359,
title={IROS 2019 Lifelong Robotic Vision: Object Recognition Challenge [Competitions]},
author={H. {Bae} and E. {Brophy} and R. H. M. {Chan} and B. {Chen} and F. {Feng}
and G. {Graffieti} and V. {Goel} and X. {Hao} and H. {Han} and S. {Kanagarajah} and S. {Kumar}
and S. {Lam} and T. L. {Lam} and C. {Lan} and Q. {Liu} and V. {Lomonaco} and L. {Ma} and D.
{Maltoni} and G. I. {Parisi} and L. {Pellegrini} and D. {Piyasena} and S. {Pu} and Q. {She}
and D. {Sheet} and S. {Song} and Y. {Son} and Z. {Wang} and T. E. {Ward} and J. {Wu} and M.
{Wu} and D. {Xie} and Y. {Xu} and L. {Yang} and Q. {Yang} and Q. {Zhong} and L. {Zhou}},
 journal={IEEE Robotics  Automation Magazine},
year={2020},
volume={27},
number={2},
pages={11-16},}

License

BSD-4-Clause

Data Summary
Type
Image,
Amount
1106.424K
Size
125.05GB
Provided by
Qi She et al.
| Amount 1106.424K | Size 125.05GB
OpenLORIS-2020
Classification
Robot
License: BSD-4-Clause

Overview

We provide a new lifelong robotic vision dataset (“OpenLORIS-Object”) collected via RGB-D cameras
mounted on mobile robots. The dataset embeds the challenges faced by a robot in the real-life
application and provides new benchmarks for validating lifelong object recognition algorithms.

Data Collection

Several grounded robots mounted by depth cameras and
other sensors are used for the data collection. These robots are
moving in the offices, homes, and malls, where the scenes are
diverse and changing all the time. In the OpenLORIS-Object
dataset, we provide the RGB-D video dataset for the objects.

The robot is actively recording the videos of targeted objects under multiple illuminations,
occlusions, camera-object distances/angles, and context information (clutters). We do include
the common challenges that the robot is usually faced with. For example,

  • Illumination. In
    a real-world application, the illumination can vary significantly across time, e.g., day and
    night differences. We repeat the data collection under weak, normal, and strong lighting conditions,
    respectively. The task becomes challenging with lights to be very weak.
  • Occlusion. Occlusion
    happens when a part of an object is hidden by other objects, or only a portion of the object
    is visible in the field of view. Since distinctive characteristics of the object might be hidden,
    occlusion significantly increases the difficulty for recognition.
  • Object size. Small-size
    or elongated objects make the task challenging, like dry batteries or glue sticks.
  • Camera-object
    angles/distances. The angles of the cameras affect the attributes detected from the object.
  • Clutter. Clutter refers to the presence of other objects in the vicinity of the considered
    object. The simultaneous presence of multiple objects may interfere with the classification
    task.

Data Format

The levels 1, 2, and 3 are ranked with increasing difficulties. For each instance at each level,
we provided 260 to 600 samples, both have RGB and depth images. Thus, the total images provided
is around 2 (RGB and depth) × 381 (mean samples per instance)× 121 (instances) × 4 (factors
per level) × 3 (difficulty levels) = 1, 106, 424 images. Also, we have provided bounding boxes
and masks for each RGB image. An example of two RGB-D frames of simple and complex clutter
with 2D bounding box and mask annotations is shown in Fig. 2. The size of images under illumination,
occlusion and clutter factors is 424×240 pixels, and the size of images under object pixel
size factor are 424×240, 320×180, 1280×720 pixels.

Citation

The data provided here is the 1st version for evaluating Lifelong Object Recognition algorithms.
Please cite our paper below in any academic work done with this dataset.

Qi She et al. "OpenLORIS-Object:
A Dataset and Benchmark towards Lifelong Object Recognition". arXiv:1911.06487, 2019
Qi She
et al., "IROS 2019 Lifelong Robotic Vision: Object Recognition Challenge [Competitions]," in
IEEE Robotics & Automation Magazine, vol. 27, no. 2, pp. 11-16, June 2020, doi: 10.1109/MRA.2020.2987186.


@inproceedings{she2019openlorisobject,
    title={ {OpenLORIS-Object}: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning},
    author={Qi She and Fan Feng and Xinyue Hao and Qihan Yang and Chuanlin Lan and Vincenzo
Lomonaco and Xuesong Shi and Zhengwei Wang and Yao Guo and Yimin Zhang and Fei Qiao and Rosa
H. M. Chan},
    booktitle={2020 International Conference on Robotics and Automation (ICRA)},
    year={2020},
    pages={4767-4773},
}
}


@article{9113359,
title={IROS 2019 Lifelong Robotic Vision: Object Recognition Challenge [Competitions]},
author={H. {Bae} and E. {Brophy} and R. H. M. {Chan} and B. {Chen} and F. {Feng}
and G. {Graffieti} and V. {Goel} and X. {Hao} and H. {Han} and S. {Kanagarajah} and S. {Kumar}
and S. {Lam} and T. L. {Lam} and C. {Lan} and Q. {Liu} and V. {Lomonaco} and L. {Ma} and D.
{Maltoni} and G. I. {Parisi} and L. {Pellegrini} and D. {Piyasena} and S. {Pu} and Q. {She}
and D. {Sheet} and S. {Song} and Y. {Son} and Z. {Wang} and T. E. {Ward} and J. {Wu} and M.
{Wu} and D. {Xie} and Y. {Xu} and L. {Yang} and Q. {Yang} and Q. {Zhong} and L. {Zhou}},
 journal={IEEE Robotics  Automation Magazine},
year={2020},
volume={27},
number={2},
pages={11-16},}

License

BSD-4-Clause

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