1. SLAM Summer School 2006Dr. Stefan Williams1 CAS Marine Systems Stefan Williams, Oscar Pizarro, Ian Mahon, Paul Rigby, Matthew Johnson-Roberson ARC Centre of Excellence for Autonomous Systems School of Aerospace, Mechanical and Mechatronic Engineering The University of Sydney

2. SLAM Summer School 2006Dr. Stefan Williams2 Overview Unmanned Underwater Vehicles Reef Monitoring Navigation Sensors Terrain Aided Navigation SLAM Classifications Future Challenges Conclusions

3. SLAM Summer School 2006Dr. Stefan Williams3 Unmanned Underwater Vehicles (UUVs) Constraints No GPS Low cost IMU Unstructured Terrain Research Challenges Sensing and Perception Localisation and Mapping Control Odyssey AUV (Image courtesy of MIT) ROV Tiburon (Image courtesy of MBARI) Phoenician wreck mosaick (Image courtesy of WHOI)

4. SLAM Summer School 2006Dr. Stefan Williams4 Reef Monitoring This project is developing algorithms and methods for modelling marine environments Long term goals are to provide a set of tools to support research into the ecologically sustainable use and protection of tropical coral reefs and other marine habitats

5. SLAM Summer School 2006Dr. Stefan Williams5 Reef Monitoring Recently returned from deployment at Capricorn Bunker Reef Group in the Southern GBR Deployed AUV at numerous locations to evaluate performance of system and collect data sets 21 data sets collected undertaking a variety of mission profiles One Tree Island Fitzroy Reef

6. SLAM Summer School 2006Dr. Stefan Williams6 Biodiversity Assessment AIMS are currently conducting biodiversity assessment in collaboration with the Western Australian Museum off the North West Cape Techniques include dredge sleds, drop cameras and towed video sleds Particular focus on sponge habitats beyond diver depths Assessment for designation of Marine Sanctuaries

7. SLAM Summer School 2006Dr. Stefan Williams7 Surveys off Ningaloo Reef Invited by AIMS to undertake surveys off the coast near Ningaloo Reef Surveys of back reef and sponge beds in up to 80m depth targeting areas based on Mandu Roughness Values shown Ningaloo Reef

8. SLAM Summer School 2006Dr. Stefan Williams8 The AUV platform Fully autonomous operation, no tether Control of vehicle performed using on- board computer Sensors include Sonar (imaging and fwd obstacle avoidance) Vision (stereo) DVL Compass Pressure Mission Time ~4 hours (up to 8 hours with current housing)

9. SLAM Summer School 2006Dr. Stefan Williams9 Vehicle Specifications Depth rating700m Size2.0 m(L) x 1.5 m(H) x 1.5 m(W) Mass200kg Maximum Speed1.2 m/s Batteries800 Wh Li-ion pack Propulsion3x150 W brushless DC thrusters Navigation Attitude/HeadingTilt (±0.5 o ), Compass (±2 o ) DepthParoscientific pressure sensor, (0.01 %) VelocityRDI Navigator ADCP (1-2mm/s) AltitudeRDI Navigator USBLTrackLink 1500 HA (0.2m range, 0.25 o ) Optical Sensing CameraProslica 12bit 1360x1024 CCD Lighting2 x 200 Ws strobe Separation~1 m between camera and light Acoustic Sensing Imaging sonarTritech Seaking Obstacle Avoidancelmagenex 852 Echo Sounder Other Sensors CTSeabird 37SBI

10. SLAM Summer School 2006Dr. Stefan Williams10 AUV Navigation A common navigation sensor used in most commercial AUV’s and many ROV’s is the Doppler Velocity Log (DVL) A DVL transmits 3 or 4 sonar beams downward from the AUV to measure the speed along and across the AUV track The AUV controller integrates heading, attitude and velocity into a dead-reckoned position estimate

11. SLAM Summer School 2006Dr. Stefan Williams11 AUV Navigation Both GPS and acoustic positioning systems can be used to assist in determining AUV position. There are several methods for this including: Long Baseline Systems (LBL) Short baseline systems (SBL) Ultra short baseline system (USBL)

12. SLAM Summer School 2006Dr. Stefan Williams12 AUV Navigation The ultrashort baseline system (USBL) requires only one transducer to be installed. This makes the ultrashort baseline system more portable

13. SLAM Summer School 2006Dr. Stefan Williams13 DVL and USBL Performance Data from a recent mission illustrates the overlapping grid patterns used for dense habitat modelling

14. SLAM Summer School 2006Dr. Stefan Williams14 DVL and USBL Performance Calibration of DVL and compass results in significantly improved error characteristics (on the order of 2% of distance travelled) Also working on methods for automatic calibration of vehicle and tracking sensors

15. SLAM Summer School 2006Dr. Stefan Williams15 Terrain Aided Navigation Terrain elevation maps available for some deployment areas Observations of altitude can be used to bound likely position of the vehicle

16. SLAM Summer School 2006Dr. Stefan Williams16 Laser Airborne Depth Sounder (LADS) Can be deployed in suitable conditions where water depth is 2-50m The infra-red component reflects from the surface The green component penetrates the water and reflects from the sea floor Depth determined from difference between the two beams

17. SLAM Summer School 2006Dr. Stefan Williams17 Laser Airborne Depth Sounder (LADS) LADS very suitable suited for reef environments We have data sets available for GBR Can be as accurate as +/-5m horizontally +/- 0.5m depth

18. SLAM Summer School 2006Dr. Stefan Williams18 Sydney Harbour Demonstrations Sydney Harbour presents an ideal environment in which to validate these algorithms Detailed bathymetric maps of the harbour are available

19. SLAM Summer School 2006Dr. Stefan Williams19 Sydney Harbour Bathymetry Sydney Harbour Bathymetry from DSTO Shallow Water Survey Bathymetric data collected using multi beam echo sounder Resolution of 1m over extent of inner harbour Darling Harbour Harbour Tunnel

20. SLAM Summer School 2006Dr. Stefan Williams20 Ship transect Ship data from DSTO, including GPS position and depth soundings taken at 5s intervals, during transect of the Harbour Particle based localisation and tracking of ship using depth soundings has been demonstrated using logged data

21. SLAM Summer School 2006Dr. Stefan Williams21 Particle Tracking of Ship Transect

22. SLAM Summer School 2006Dr. Stefan Williams22 Particle Tracking of Ship Transect Lost

23. SLAM Summer School 2006Dr. Stefan Williams23 The SLAM Problem Simultaneous Localisation and Map Building (SLAM) Start at an unknown location with no a priori knowledge of landmark locations From relative observations of landmarks, compute estimate of vehicle location and estimate of landmark locations While continuing in motion, build complete map of landmarks and use these to provide continuous estimates of vehicle location

24. SLAM Summer School 2006Dr. Stefan Williams24 The Estimation Process Recursive three stage update procedure using Extended Kalman Filter (EKF)  Prediction –Use vehicle model to predict vehicle position  Observation –Take feature observation(s)  Update –Validated observations used to generate optimal estimate –Initialise new target

25. SLAM Summer School 2006Dr. Stefan Williams25 SLAM 1. Initialize vehicle at origin 2. Take sonar observation of Range Bearing 3. Initialize estimate of features and project into visual frame 4. Identify visual feature within sonar footprint 5. Track visual features using KLT and provide elevation azimuth observations to filter

26. SLAM Summer School 2006Dr. Stefan Williams26 Fusing Vision and Sonar Using the sonar returns to initialise visual features Features are then tracked from frame to frame using KLT Observations provided to SLAM algorithm to build terrain model and estimate vehicle motion KLT features are not stable over long term

27. SLAM Summer School 2006Dr. Stefan Williams27 Terrain Model Sonar returns outside of visual frames are used to generate a coarse surface map Visual frames are projected onto surface Correspondence at seams suggests that algorithm is performing well Resulting terrain model

28. SLAM Summer School 2006Dr. Stefan Williams28 Terrain Models

29. SLAM Summer School 2006Dr. Stefan Williams29 Stereo Imaging High resolution stereo pairs allow local surface to be reconstructed Alternatively, features in images can be identified and position relative to vehicle computed Developing techniques for integration into SLAM framework

30. SLAM Summer School 2006Dr. Stefan Williams30 Stereo Imaging

31. SLAM Summer School 2006Dr. Stefan Williams31 Stereo Imaging

32. SLAM Summer School 2006Dr. Stefan Williams32 Stereo Imaging

33. SLAM Summer School 2006Dr. Stefan Williams33 Sponge Beds at 80m off Ningaloo

34. SLAM Summer School 2006Dr. Stefan Williams34 Visual Mosaics at 40m off Ningaloo

35. SLAM Summer School 2006Dr. Stefan Williams35 Terrain Classification Techniques for automatic terrain classification Combined with terrain models provide a mechanism for estimating cover Preliminary work looked at the use of Gray Scale Histograms and mean RGB colour

36. SLAM Summer School 2006Dr. Stefan Williams36 Terrain Classification Selection of a richer description of texture is required Gabor filter is used a varying scales and orientation to identify patches of coral, sand and other terrain in images Investigating combination of visual and sonar data for classification

37. SLAM Summer School 2006Dr. Stefan Williams37 Terrain Classification

38. SLAM Summer School 2006Dr. Stefan Williams38 Autonomous Systems Roles: Wide-Area Surveillance Situational Awareness Air/Land/Sea Operations Tactical Strike Mine Hunting Littoral Zone Support and Sensor Payload Delivery Research Challenges: Multi-Sensor Data Fusion Multiple Platform Control Autonomous Operations Systems of Systems Future Challenges: Defence Applications

39. SLAM Summer School 2006Dr. Stefan Williams39 Future Challenges: Integrated Ocean Observatories Autonomous Systems Roles: Wide-Area Surveillance Situational Awareness Air/Land/Sea Operations Research Challenges: Multi-Sensor Data Fusion Modelling Ocean Processes Multiple Platform Control Autonomous Operations Systems of Systems

40. SLAM Summer School 2006Dr. Stefan Williams40 Autonomous Systems Roles: Exploration Mapping Extraction Research Challenges: Multi-Sensor Data Fusion Adaptive Sensing Future Challenges: Resource Exploration

41. SLAM Summer School 2006Dr. Stefan Williams41 Conclusions Terrain aided navigation is feasible Extension to unstructured terrain map building on Autonomous Underwater Vehicle (AUV) Deployment in various fielded situations Applications Ecological Monitoring Mineral Exploration Mine Hunting Littoral Zone Support and Sensor Payload Delivery

42. SLAM Summer School 2006Dr. Stefan Williams42 Acknowledgements This work is supported in part by the Australian Research Council (ARC) and the New South Wales Government Additional funding has been received from our industry partners, including BAe Systems ATC Sonartech Atlas The Great Barrier Reef Research Foundation The Commonwealth of Australia DSTO

43. SLAM Summer School 2006Dr. Stefan Williams43