Autonomous Navigation for Unmanned Underwater Vehicles: Real-Time Experiments Using Computer Vision

This letter studies the problem of autonomous navigation for unmanned underwater vehicles, using computer vision for localization. Parallel tracking and mapping is employed to localize the vehicle with respect to a visual map, using a single camera, whereas an extended Kalman filter (EKF) is used to fuse the visual information with data from an inertial measurement unit, in order to recover the scale of the map and improve the pose estimation. A proportional integral derivative controller controller with compensation of the restoring forces is proposed to accomplish trajectory tracking, where a pressure sensor and a magnetometer provide feedback for depth control and yaw, respectively, while the remaining states are provided by the EKF. Real-time experiments are presented to validate the navigation strategy, using a commercial remotely operated vehicle (ROV), the BlueROV2, which was adapted to perform as an autonomous underwater vehicle with the help of the robot operative system.

[1]  Silvia Silva da Costa Botelho,et al.  Underwater SLAM: Challenges, state of the art, algorithms and a new biologically-inspired approach , 2014, 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics.

[2]  David W. Murray,et al.  Parallel Tracking and Mapping on a camera phone , 2009, 2009 8th IEEE International Symposium on Mixed and Augmented Reality.

[3]  Roland Siegwart,et al.  Real-time metric state estimation for modular vision-inertial systems , 2011, 2011 IEEE International Conference on Robotics and Automation.

[4]  N. Gracias,et al.  Detection of interest points in turbid underwater images , 2011, OCEANS 2011 IEEE - Spain.

[5]  Javier Civera,et al.  Real-time localization and dense mapping in underwater environments from a monocular sequence , 2015, OCEANS 2015 - Genova.

[6]  Sei Ikeda,et al.  Visual SLAM algorithms: a survey from 2010 to 2016 , 2017, IPSJ Transactions on Computer Vision and Applications.

[7]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.

[8]  Silvia Silva da Costa Botelho,et al.  An Open-source Bio-inspired Solution to Underwater SLAM , 2015 .

[9]  Anibal Matos,et al.  Survey on advances on terrain based navigation for autonomous underwater vehicles , 2017 .

[10]  Gabriel Oliver,et al.  Cluster-based loop closing detection for underwater slam in feature-poor regions , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Bruce A. MacDonald,et al.  A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera , 2012, Remote. Sens..

[12]  Roland Siegwart,et al.  Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV , 2012, 2012 IEEE International Conference on Robotics and Automation.

[13]  Aníbal Matos,et al.  Tracking multiple Autonomous Underwater Vehicles , 2019, Auton. Robots.

[14]  Thor I. Fossen,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control: Fossen/Handbook of Marine Craft Hydrodynamics and Motion Control , 2011 .

[15]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[16]  Sajad Saeedi,et al.  AUV Navigation and Localization: A Review , 2014, IEEE Journal of Oceanic Engineering.

[17]  J Snyder,et al.  Doppler Velocity Log (DVL) navigation for observation-class ROVs , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[18]  Mohammad H. Marhaban,et al.  Review of visual odometry: types, approaches, challenges, and applications , 2016, SpringerPlus.

[19]  Sergio Salazar,et al.  Design and Control of an Autonomous Underwater Vehicle (AUV-UMI) , 2018 .