Update README.md

README.md

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-license: bsd-3-clause
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+# DeepVL training dataset
+
+---
+license: bsd-3-clause
+---
+
+## Introduction
+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.
+
+## Dataset details
+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:
+
+```
+trajX/
+├── alphasense_imu_data.npy          # IMU data from Alphasense Sensense             | rate: 200Hz
+├── biases_data.npy                  # Estimated IMU biases (from ReAqROVIO)         | rate:  20Hz
+├── fcu_imu_data.npy                 # IMU data from flight control unit             | rate: 200Hz
+├── gravity_b_vec.npy                # Gravity vector in body frame                  | rate:  20Hz
+├── motor_commands_data.npy          # Motor command PWM signals for all 8 thrusters | rate: 200Hz
+├── orientation_data_Rmat.npy        # Orientation matrices (body to world)          | rate:  20Hz
+├── supervision_odom_data.npy        # Ground-truth odometry (from ReAqROVIO)        | rate:  20Hz
+├── battery_data.npy                 # (Optional) Battery voltage and current        | rate:  20Hz
+```
+
+Each file is in `.npy` format and can be loaded and parsed using numpy. In each numpy file the data is organized as:
+
+```
+[data_column_1, data_column_2, ... data_column_N, time_stamp_column]
+```
+
+## Contact
+For questions or support, contact authors:
+
+* [Mohit Singh](mailto:mohit.singh@ntnu.no)
+* [Kostas Alexis](mailto:konstantinos.alexis@ntnu.no)