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--- |
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license: bsd-3-clause |
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task_categories: |
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- robotics |
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language: |
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- en |
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tags: |
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- deepvl |
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- underwater |
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- odometry |
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--- |
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# DeepVL training dataset |
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## Introduction |
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This dataset repository contains the training and testing datasets used in the paper: ["DeepVL: Dynamics and Inertial Measurements-based Deep Velocity Learning for Underwater Odometry"](https://ntnu-arl.github.io/deepvl-deep-velocity-learning/). The dataset was collected by manually pilotting an underwater robot in a pool and in the Trondhiem fjord. |
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## Dataset details |
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The training data is located in the `train_full` directory and the test data in `test` directory respectively. The training data directory contains trajectories from `traj1` to `traj12`, and testing data contains from `traj1` to `traj2`. Each trajectory contains files described as follows: |
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``` |
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trajX/ |
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├── alphasense_imu_data.npy # IMU data from Alphasense Sensense | rate: 200Hz |
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├── biases_data.npy # Estimated IMU biases (from ReAqROVIO) | rate: 20Hz |
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├── fcu_imu_data.npy # IMU data from flight control unit | rate: 200Hz |
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├── gravity_b_vec.npy # Gravity vector in body frame | rate: 20Hz |
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├── motor_commands_data.npy # Motor command PWM signals for all 8 thrusters | rate: 200Hz |
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├── orientation_data_Rmat.npy # Orientation matrices (body to world) | rate: 20Hz |
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├── supervision_odom_data.npy # Ground-truth odometry (from ReAqROVIO) | rate: 20Hz |
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├── battery_data.npy # (Optional) Battery voltage and current | rate: 20Hz |
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``` |
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Each file is in `.npy` format and can be loaded and parsed using numpy. In each numpy file the data is organized as: |
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``` |
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[data_column_1, data_column_2, ... data_column_N, time_stamp_column] |
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``` |
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## Contact |
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For questions or support, contact authors: |
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* [Mohit Singh](mailto:[email protected]) |
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* [Kostas Alexis](mailto:[email protected]) |
