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README.md
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# DeepVL training dataset
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---
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license: bsd-3-clause
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---
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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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├── 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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## Contact
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For questions or support, contact authors:
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* [Mohit Singh](mailto:mohit.singh@ntnu.no)
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* [Kostas Alexis](mailto:konstantinos.alexis@ntnu.no)
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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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├── 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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## Contact
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For questions or support, contact authors:
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* [Mohit Singh](mailto:mohit.singh@ntnu.no)
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* [Kostas Alexis](mailto:konstantinos.alexis@ntnu.no)
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