README.md

metadata

license: bsd-3-clause
task_categories:
  - robotics
language:
  - en
tags:
  - deepvl
  - underwater
  - odometry

DeepVL training dataset

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". 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
• Kostas Alexis