Dataset Open Access

Event-Based Visual Place Recognition With Ensembles of Temporal Windows

Fischer, Tobias; Milford, Michael

This dataset accompanies the following publication, please cite this publication if you use this dataset:

Fischer, T. and Milford, M., 2020. Event-Based Visual Place Recognition With Ensembles of Temporal Windows. IEEE Robotics and Automation Letters5(4), pp.6924-6931.

@article{fischer2020event,
  title={Event-Based Visual Place Recognition With Ensembles of Temporal Windows},
  author={Fischer, Tobias and Milford, Michael},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={4},
  pages={6924--6931},
  year={2020}
}

The dataset contains six sequences of recordings. For each recording, four files are made available:

  1. A rosbag (*.bag) file with the following contents:
    • /dvs/events (type: dvs_msgs/EventArray) with the event stream, see https://github.com/uzh-rpg/rpg_dvs_ros)

    • /dvs/camera_info (type: sensor_msgs/CameraInfo) with the camera info of the DAVIS frame camera

    • /dvs/image_raw (type: sensor_msgs/Image) with the DAVIS frame camera images

    • /dvs/imu (sensor_msgs/Imu) with the IMU data of the event camera

  2. An associated *_hot_pixels.txt file that contains the hot pixels for that recording (detected with https://github.com/cedric-scheerlinck/dvs_tools/tree/master/dvs_hot_pixel_filter)

  3. The recordings using a frame-based consumer camera (*.mp4 files)

  4. Associated GPS information (*.nmea) files recorded using the consumer camera - synchronized with the mp4 files.

 

Note that the timestamps of the event files and frame-based camera files do not match as they were not synchronized during recording time. Please see the associated code repository (https://github.com/Tobias-Fischer/ensemble-event-vpr) for the correct mappings and time offsets between the event recordings and frame-based recordings.

The code repository also contains code to match GPS data between different traverses.

This work received funding from the Australian Government, via grant AUSMURIB000001 associated with ONR MURI grant N00014-19-1-2571. The authors acknowledge continued support from the Queensland University of Technology (QUT) through the Centre for Robotics.
Files (85.2 GB)
Name Size
20200421_170039-sunset1_concat.mp4
md5:92c91146c317afcaf711200ec4724905
1.2 GB Download
20200421_170039-sunset1_concat.nmea
md5:11f0107a4df845fd315e9134fbee5c1e
408.3 kB Download
20200422_172431-sunset2_concat.mp4
md5:419bd2c50aa5696c0a84db4374d8aa73
895.5 MB Download
20200422_172431-sunset2_concat.nmea
md5:ff879bf22a9552a6d8500a98cff6c7f9
356.3 kB Download
20200424_151015-daytime_concat.mp4
md5:7aa23137ba75b4fcdf5a4be5d1bd960b
1.4 GB Download
20200424_151015-daytime_concat.nmea
md5:867fdf43ef393ac7e8de251c1a5cd585
391.2 kB Download
20200427_181204-night_concat.mp4
md5:a0e87f8206caed2889585e255b7c41c2
249.7 MB Download
20200427_181204-night_concat.nmea
md5:441e6673e0dfc8746f76cd646c4aba8d
371.3 kB Download
20200428_091154-morning_concat.mp4
md5:77a88e84a4872bda02381b641848bbdf
1.2 GB Download
20200428_091154-morning_concat.nmea
md5:b86af464ceac478711e52ef4271c198c
381.7 kB Download
20200429_061912-sunrise_concat.mp4
md5:5e9edd6c3f177a9bdbe7b9c7b8b6b6c4
1.2 GB Download
20200429_061912-sunrise_concat.nmea
md5:ec04cf35c10eb5b519b11297adef024b
376.0 kB Download
dvs_vpr_2020-04-21-17-03-03.bag
md5:04473f623aec6bda3d7eadfecfc1b2ce
15.1 GB Download
dvs_vpr_2020-04-21-17-03-03_hot_pixels.txt
md5:b1b37527a8275964ba37a9dcec9a1f11
365 Bytes Download
dvs_vpr_2020-04-22-17-24-21.bag
md5:ca6db080a4054196fe65825bce3db351
14.9 GB Download
dvs_vpr_2020-04-22-17-24-21_hot_pixels.txt
md5:2ddf8a73c90afcde02bb7a48409e5a3e
281 Bytes Download
dvs_vpr_2020-04-24-15-12-03.bag
md5:909569732e323ff04c94379a787f2a69
16.9 GB Download
dvs_vpr_2020-04-24-15-12-03_hot_pixels.txt
md5:00e00b0166c84dd15a4fc3186d8c623b
248 Bytes Download
dvs_vpr_2020-04-27-18-13-29.bag
md5:e80b6c0434690908d855445792d4de3b
5.4 GB Download
dvs_vpr_2020-04-27-18-13-29_hot_pixels.txt
md5:e7660b90e9628cce6213a23916af96e5
323 Bytes Download
dvs_vpr_2020-04-28-09-14-11.bag
md5:7854ede61c0947adb0f072a041dc3bad
12.5 GB Download
dvs_vpr_2020-04-28-09-14-11_hot_pixels.txt
md5:af893eb324b7f672f3c5dd2933ac5894
240 Bytes Download
dvs_vpr_2020-04-29-06-20-23.bag
md5:d7ccfeb6539f1e7b077ab4fe6f45193c
14.2 GB Download
dvs_vpr_2020-04-29-06-20-23_hot_pixels.txt
md5:f95ce15f47ae22fae999d229d9d073c7
289 Bytes Download
  • Fischer, T. and Milford, M., 2020. Event-Based Visual Place Recognition With Ensembles of Temporal Windows. IEEE Robotics and Automation Letters, 5(4), pp.6924-6931.

433
901
views
downloads
Views 433
Downloads 901
Data volume 7.1 TB
Unique views 377
Unique downloads 288

Share

Cite as