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:
264 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

 

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This study presents an overview of AiDIN-VI, a force-controllable quadruped robot system that is incorporated the mandatory abilities: speed, efficiency, and mobility to provide real-world services. 

The paper describes design methodologies and principles for implementing the requisite capabilities in a single robot platform, and in particular, the torque sensing method, components, and modularization method of the torque-controllable actuator unit.

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This dataset provides digital images and videos of surface ice conditions were collected from two Alberta rivers - North Saskatchewan River and Peace River - in the 2016-2017 winter seasons.

Images from North Saskatchewan River were collected using both Reconyx PC800 Hyperfire Professional game cameras mounted on two bridges in Edmonton as well as a Blade Chroma UAV equipped with a CGO3 4K camera at the Genesee boat launch.

Data for the Peace River was collected using only the UAV at the Dunvegan Bridge boat launch and Shaftesbury Ferry crossing.

Instructions: 

Python code and instructions for using the dataset are available in this repository: https://github.com/abhineet123/river_ice_segmentation

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This set of data provides test results for designers and facility operators to apply in determining the arc-flash hazard distance and the incident energy to which employees could be exposed during their work on or near electrical equipment. 

Instructions: 

In order to accurately predict the response time for protective devices, the arcing current and all other variables, please refer to IEEE Std 1584-2018 IEEE Guide for Performing Arc-Flash Hazards Calculations and manufacturer’s written documentation.

The IEEE Std 1584-2018 IEEE Guide for Performing Arc-Flash Hazards Calculations includes a description of all test programs and a collection of the test data that have been used in the development of such consensus based document. 

All test were conducted at high power laboratories for the purpose of developing an understanding of the electrical characteristics of arc flashes and the resultant incident energy. Such test results, as stored in this IEEE DataPort tool could be used to develop empirically based equations or to verify physical model based equations.

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