First Name: 
Andrejs
Last Name: 
Zujevs

Datasets & Competitions

A new generation of computer vision, namely event-based or neuromorphic vision, provides a new paradigm for capturing visual data and the way such data is processed. Event-based vision is a state-of-art technology of robot vision. It is particularly promising for use in both mobile robots and drones for visual navigation tasks. Due to a highly novel type of visual sensors used in event-based vision, only a few datasets aimed at visual navigation tasks are publicly available.

Instructions: 

The dataset includes the following sequences:

  • 01_winter_forest – Daytime, No wind, Clear weather, Snowy scenery, Closed loop, Forest trail
  • 02_winter_forest - Daytime, No wind, Clear weather, Snowy scenery, Closed loop, Forest trail
  • 03_winter_parking_lot - Daytime, No wind, Clear weather, Snowy scenery, Closed loop, Asphalt road
  • 04_winter_bush_rows - Daytime, No wind, Snowy scenery, Closed loop, Shrubland
  • 05_winter_bush_rows - Daytime, No wind, Snowy scenery, Closed loop, Shrubland
  • 06_winter_greenhouse_complex - Daytime, No wind, Snowy scenery, Closed loop, Cattle farm feed table
  • 07_winter_greenhouse_complex - Daytime, No wind, Snowy scenery, Closed loop, Cattle farm feed table
  • 08_winter_orchard - Daytime, No wind, Snowy scenery, Closed loop, Orchard
  • 09_winter_orchard - Daytime, No wind, Snowy scenery, Closed loop, Orchard
  • 10_winter_farm - Daytime, No wind, Snowy scenery, Closed loop, Cattle farm feed table
  • 11_winter_farm - Daytime, No wind, Snowy scenery, Closed loop, Cattle farm feed table
  • 12_summer_bush_rows - Daytime, Mild wind, Closed loop, Shrubland
  • 13_summer_bush_rows - Daytime, Mild wind, Closed loop, Shrubland
  • 14_summer_farm - Daytime, Mild wind, Closed loop, Shrubland, Tilled field
  • 15_summer_farm - Daytime, Mild wind, Closed loop, Shrubland, Tilled field
  • 16_summer_orchard - Daytime, Mild wind, Closed loop, Shrubland, Orchard
  • 17_summer_orchard - Daytime, Mild wind, Closed loop, Shrubland, Orchard
  • 18_summer_garden - Daytime, Mild wind, Closed loop, Pine coppice, Winter wheat sowing, Winter rapeseed
  • 19_summer_garden - Daytime, Mild wind, Closed loop, Pine coppice, Winter wheat sowing, Winter rapeseed
  • 20_summer_farm - Daytime, Mild wind, Closed loop, Orchard, Tilled field, Cows tethered in pasture
  • 21_summer_farm - Daytime, Mild wind, Closed loop, Orchard, Tilled field, Cows tethered in pasture
  • 22_summer_hangar - Daytime, No wind, Closed loop
  • 23_summer_hangar - Daytime, No wind, Closed loop
  • 24_summer_hangar - Daytime, No wind, Closed loop
  • 25_summer_puddles - Daytime, No wind, Closed loop, Meadow, grass up to 30 cm
  • 26_summer_green_meadow - Daytime, No wind, Closed loop, Meadow, grass up to 30 cm
  • 27_summer_green_meadow - Daytime, No wind, Closed loop, Meadow, grass up to 30 cm
  • 28_summer_grooved_field - Daytime, No wind, Closed loop, Meadow, grass up to 100 cm, Furrows (longitudinally and transversely)
  • 29_summer_grooved_field - Daytime, No wind, Closed loop, Meadow, grass up to 100 cm, Furrows (longitudinally and transversely)
  • 30_summer_grooved_field - Daytime, No wind, Closed loop, Furrows (longitudinally and transversely)
  • 31_summer_grooved_field - Daytime, No wind, Closed loop, Furrows (longitudinally and transversely)
  • 32_summer_cereal_field - Daytime, No wind, Closed loop, Meadow, grass up to 100 cm
  • 33_summer_cereal_field - Daytime, No wind, Closed loop, Meadow, grass up to 100 cm
  • 34_summer_forest - Daytime, No wind, Closed loop, Forest trail
  • 35_summer_forest - Daytime, No wind, Closed loop, Forest trail
  • 36_summer_forest - Daytime, No wind, Closed loop, Forest trail, Forest surface - moss, branches, stumps
  • 37_summer_forest - Daytime, No wind, Closed loop, Forest trail, Forest surface - moss, branches, stumps
  • 38_summer_dark_parking_lot - Twilight, No wind, Closed loop, Asphalt road, Lawn
  • 39_summer_dark_parking_lot - Twilight, No wind, Closed loop, Asphalt road, Lawn
  • 40_summer_parking_lot - Daytime, Mild wind, Closed loop, Asphalt road, Lawn
  • 41_summer_greenhouse - Daytime, Closed loop, Greenhouse
  • 42_summer_greenhouse - Daytime, Closed loop, Greenhouse

Each sequence contains the following separately downloadable files:

  • <..sequence_id..>_video.mp4 – provides an overview of the sequence data (for the DVS and RGB-D sensors).
  • <..sequence_id..>_data.tar.gz – entire date sequence in raw data format (AEDAT2.0 - DVS, images - RGB-D, point clouds in pcd files - LIDAR, and IMU csv files with original sensor timestamps). Timestamp conversion formulas are available.
  • <..sequence_id..>_rawcalib_data.tar.gz – recorded fragments that can be used to perform the calibration independently (intrinsic, extrinsic and time alignment).
  • <..sequence_id..>_rosbags.tar.gz – main sequence in ROS bag format. All sensors timestamps are aligned with DVS with an accuracy of less than 1 ms.

The contents of each archive are described below..

Raw format data

The archive <..sequence_id..>_data.tar.gz contains the following files and folders:

  • ./meta-data/ - all the useful information about the sequence
  • ./meta-data/meta-data.md - detailed information about the sequence, sensors, files, and data formats
  • ./meta-data/cad_model.pdf - sensors placement
  • ./meta-data/<...>_timeconvs.json - coefficients for timestamp conversion formulas
  • ./meta-data/ground-truth/ - movement ground-truth data, calculated using 3 different Lidar-SLAM algorithms (Cartographer, HDL-Graph, LeGo-LOAM)
  • ./meta-data/calib-params/ - intrinsic and extrinsic calibration parameters
  • ./recording/ - main sequence
  • ./recording/dvs/ - DVS events and IMU data
  • ./recording/lidar/ - Lidar point clouds and IMU data
  • ./recording/realsense/ - Realsense camera RGB, Depth frames, and IMU data
  • ./recording/sensorboard/ - environmental sensors data (temperature, humidity, air pressure)

Calibration data

The <..sequence_id..>_rawcalib_data.tar.gz archive contains the following files and folders:

  • ./imu_alignments/ - IMU recordings of the platform lifting before and after the main sequence (can be used for custom timestamp alignment)
  • ./solenoids/ - IMU recordings of the solenoid vibrations before and after the main sequence (can be used for custom timestamp alignment)
  • ./lidar_rs/ - Lidar vs Realsense camera extrinsic calibration by showing both sensors a spherical object (ball)
  • ./dvs_rs/ - DVS and Realsense camera intrinsic and extrinsic calibration frames (checkerboard pattern)

ROS Bag format data

There are six rosbag files for each scene, their contents are as follows:

  • <..sequence_id..>_dvs.bag (topics: /dvs/camera_info, /dvs/events, /dvs/imu, and accordingly message types: sensor_msgs/CameraInfo, dvs_msgs/EventArray, sensor_msgs/Imu).
  • <..sequence_id..>_lidar.bag (topics: /lidar/imu/acc, /lidar/imu/gyro, /lidar/pointcloud, and accordingly message types: sensor_msgs/Imu, sensor_msgs/Imu, sensor_msgs/PointCloud2).
  • <..sequence_id..>_realsense.bag (topics: /realsense/camera_info, /realsense/depth, /realsense/imu/acc, /realsense/imu/gyro, /realsense/rgb, /tf, and accordingly message types: sensor_msgs/CameraInfo, sensor_msgs/Image, sensor_msgs/Imu, sensor_msgs/Imu, sensor_msgs/Image, tf2_msgs/TFMessage).
  • <..sequence_id..>_sensorboard.bag (topics: /sensorboard/air_pressure, /sensorboard/relative_humidity, /sensorboard/temperature, and accordingly message types: sensor_msgs/FluidPressure, sensor_msgs/RelativeHumidity, sensor_msgs/Temperature).
  • <..sequence_id..>_trajectories.bag (topics: /cartographer, /hdl, /lego_loam, and accordingly message types: geometry_msgs/PoseStamped, geometry_msgs/PoseStamped, geometry_msgs/PoseStamped).
  • <..sequence_id..>_data_for_realsense_lidar_calibration.bag (topics: /lidar/pointcloud, /realsense/camera_info, /realsense/depth, /realsense/rgb, /tf, and accordingly message types: sensor_msgs/PointCloud2, sensor_msgs/CameraInfo, sensor_msgs/Image, sensor_msgs/Image, tf2_msgs/TFMessage).
Categories:
255 Views

A new generation of computer vision, namely event-based or neuromorphic vision, provides a new paradigm for capturing visual data and the way such data is processed. Event-based vision is a state-of-art technology of robot vision. It is particularly promising for use in both mobile robots and drones for visual navigation tasks. Due to a highly novel type of visual sensors used in event-based vision, only a few datasets aimed at visual navigation tasks are publicly available.

Instructions: 

The dataset includes the following sequences:

  • 01_forest – Closed loop, Forest trail, No wind, Daytime
  • 02_forest – Closed loop, Forest trail, No wind, Daytime
  • 03_green_meadow – Closed loop, Meadow, grass up to 30 cm, No wind, Daytime
  • 04_green_meadow – Closed loop, Meadow, grass up to 30 cm, Mild wind, Daytime
  • 05_road_asphalt – Closed loop, Asphalt road, No wind, Nighttime
  • 06_plantation – Closed loop, Shrubland, Mild wind, Daytime
  • 07_plantation – Closed loop, Asphalt road, No wind, Nighttime
  • 08_plantation_water – Random movement, Sprinklers (water drops on camera lens), No wind, Nighttime
  • 09_cattle_farm – Closed loop, Cattle farm, Mild wind, Daytime
  • 10_cattle_farm – Closed loop, Cattle farm, Mild wind, Daytime
  • 11_cattle_farm_feed_table – Closed loop, Cattle farm feed table, Mild wind, Daytime
  • 12_cattle_farm_feed_table – Closed loop, Cattle farm feed table, Mild wind, Daytime
  • 13_ditch – Closed loop, Sandy surface, Edge of ditch or drainage channel, No wind, Daytime
  • 14_ditch – Closed loop, Sandy surface, Shore or bank, Strong wind, Daytime
  • 15_young_pines – Closed loop, Sandy surface, Pine coppice, No wind, Daytime
  • 16_winter_cereal_field – Closed loop, Winter wheat sowing, Mild wind, Daytime
  • 17_winter_cereal_field – Closed loop, Winter wheat sowing, Mild wind, Daytime
  • 18_winter_rapeseed_field – Closed loop, Winter rapeseed, Mild wind, Daytime
  • 19_winter_rapeseed_field – Closed loop, Winter rapeseed, Mild wind, Daytime
  • 20_field_with_a_cow – Closed loop, Cows tethered in pasture, Mild wind, Daytime
  • 21_field_with_a_cow – Closed loop, Cows tethered in pasture, Mild wind, Daytime

Each sequence contains the following separately downloadable files:

  • <..sequence_id..>_video.mp4 – provides an overview of the sequence data (for the DVS and RGB-D sensors).
  • <..sequence_id..>_data.tar.gz – entire date sequence in raw data format (AEDAT2.0 - DVS, images - RGB-D, point clouds in pcd files - LIDAR, and IMU csv files with original sensor timestamps). Timestamp conversion formulas are available.
  • <..sequence_id..>_rawcalib_data.tar.gz – recorded fragments that can be used to perform the calibration independently (intrinsic, extrinsic and time alignment).
  • <..sequence_id..>_rosbags.tar.gz – main sequence in ROS bag format. All sensors timestamps are aligned with DVS with an accuracy of less than 1 ms.

The contents of each archive are described below..

Raw format data

The archive <..sequence_id..>_data.tar.gz contains the following files and folders:

  • ./meta-data/ - all the useful information about the sequence
  • ./meta-data/meta-data.md - detailed information about the sequence, sensors, files, and data formats
  • ./meta-data/cad_model.pdf - sensors placement
  • ./meta-data/<...>_timeconvs.json - coefficients for timestamp conversion formulas
  • ./meta-data/ground-truth/ - movement ground-truth data, calculated using 3 different Lidar-SLAM algorithms (Cartographer, HDL-Graph, LeGo-LOAM)
  • ./meta-data/calib-params/ - intrinsic and extrinsic calibration parameters
  • ./recording/ - main sequence
  • ./recording/dvs/ - DVS events and IMU data
  • ./recording/lidar/ - Lidar point clouds and IMU data
  • ./recording/realsense/ - Realsense camera RGB, Depth frames, and IMU data
  • ./recording/sensorboard/ - environmental sensors data (temperature, humidity, air pressure)

Calibration data

The <..sequence_id..>_rawcalib_data.tar.gz archive contains the following files and folders:

  • ./imu_alignments/ - IMU recordings of the platform lifting before and after the main sequence (can be used for custom timestamp alignment)
  • ./solenoids/ - IMU recordings of the solenoid vibrations before and after the main sequence (can be used for custom timestamp alignment)
  • ./lidar_rs/ - Lidar vs Realsense camera extrinsic calibration by showing both sensors a spherical object (ball)
  • ./dvs_rs/ - DVS and Realsense camera intrinsic and extrinsic calibration frames (checkerboard pattern)

ROS Bag format data

There are six rosbag files for each scene, their contents are as follows:

  • <..sequence_id..>_dvs.bag (topics: /dvs/camera_info, /dvs/events, /dvs/imu, and accordingly message types: sensor_msgs/CameraInfo, dvs_msgs/EventArray, sensor_msgs/Imu).
  • <..sequence_id..>_lidar.bag (topics: /lidar/imu/acc, /lidar/imu/gyro, /lidar/pointcloud, and accordingly message types: sensor_msgs/Imu, sensor_msgs/Imu, sensor_msgs/PointCloud2).
  • <..sequence_id..>_realsense.bag (topics: /realsense/camera_info, /realsense/depth, /realsense/imu/acc, /realsense/imu/gyro, /realsense/rgb, /tf, and accordingly message types: sensor_msgs/CameraInfo, sensor_msgs/Image, sensor_msgs/Imu, sensor_msgs/Imu, sensor_msgs/Image, tf2_msgs/TFMessage).
  • <..sequence_id..>_sensorboard.bag (topics: /sensorboard/air_pressure, /sensorboard/relative_humidity, /sensorboard/temperature, and accordingly message types: sensor_msgs/FluidPressure, sensor_msgs/RelativeHumidity, sensor_msgs/Temperature).
  • <..sequence_id..>_trajectories.bag (topics: /cartographer, /hdl, /lego_loam, and accordingly message types: geometry_msgs/PoseStamped, geometry_msgs/PoseStamped, geometry_msgs/PoseStamped).
  • <..sequence_id..>_data_for_realsense_lidar_calibration.bag (topics: /lidar/pointcloud, /realsense/camera_info, /realsense/depth, /realsense/rgb, /tf, and accordingly message types: sensor_msgs/PointCloud2, sensor_msgs/CameraInfo, sensor_msgs/Image, sensor_msgs/Image, tf2_msgs/TFMessage).

Version history

22.06.2021.

  • Realsense data now also contain depth png images with 16-bit depth, which are located in folder /recording/realsense/depth_native/
  • Added data in rosbag format

 

Categories:
330 Views