This dataset presents a collection of coordinates that belongs to paths generated with a 3D disjstkra algorithm,in diferents enviroments,with a grid size equal to one. The output is a six dimension vector that represents the action taken by the agent (z+,z-,y+,y-,x+,x-) based on his pose, sensors readings and the target. 

 

Categories:
145 Views

The iSAID-Reduce100 is a reduced version of the DOTA dataset for instance segmentation task, including 1400 training samples and 1362 validation samples. The images are captured from multiple sensors and cropped to (512, 512).  

Categories:
69 Views

The dataset consists of training and test data and label matrices for single-pixel compressive DoA estimation for mmWave metasurface. The dataset will be uploaded soon. Currently, a small part of the dataset can be accessed through this repository. For detailed information, please visit Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation through https://arxiv.org/abs/2109.05466

Categories:
109 Views

In our study, datasets of two simulators, namely phasor-based simulator and hybrid-type simulator are used. In the hybrid environment, first, the outputs of the phasor-based simulator are converted to instantaneous waveforms, then based on instruction, distortions and noises are added (superimposed) to these waveforms, and finally, the distorted waveforms are fed to the detailed model of PMUs simulated in EMT domain. Outputs of both simulators can be found in the submitted file.

Categories:
198 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_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:
288 Views

The availability of labelled Cyber Bulling Types dataset has been exhibited for high profile Natural Language Processing (NLP), which constantly leads the advancement of constructing and model creation-based text. I aim at extracting diverse and efficient Cyber Bully Tweets from the Twitter Social Media Platform. This dataset contains 5 types of cyber bullying samples. They are

1.    Sexual Harassment

2.    Doxing

3.    Cyberstalking

Categories:
784 Views

Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence ``Large rooms and great breakfast", ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks.

Categories:
78 Views

The dataset contains the navigation measurements obtained in the indoor experiment field. The volunteers move on the whole 4th floor of the Building D of Dong Jiu Teaching classes at Huazhong University of Science and Technology. Meanwhile, the experimental area consists of a total area of 717 m 2. These datasets were used and can be used to test and validate the radio map database updating-based localization positioning algorithm through the RSSI signals space.

Categories:
529 Views

Most of Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for an effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques have been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems.

Categories:
556 Views

The file contains the source code of the mission generator MG1 together with the materials and results of the experiments. This data is associated with a paper in which we focus on an experimentalprotocol for the comparison of fully-automatic design methods of control software for robot swarms.

Categories:
39 Views

Pages