104 research outputs found
Mid-water Localisation for Autonomous Underwater Vehicles
Survey-class Autonomous Underwater Vehicles (AUVs) rely on Doppler Velocity Logs (DVL) for precise localisation and navigation near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localising between the surface, where GPS is available, and the seafloor presents a localisation problem since both GPS and DVL are unavailable in the mid-water column. Reliance on acoustic tracking methods such as Ultra Short Base Line (USBL) requires a ship to track the vehicle, while Long Base Line (LBL) requires the setting up of an acoustic transponder network. These methods provide bounded error position localisation (~10m) of the underwater vehicle, but inhibits the flexibility and autonomy of the vehicle due to tending or set-up requirements. Proposed alternatives to these include combining GPS on the surface, navigation-grade IMU, the DVL water-track mode and a vehicle model to reduce the dead-reckoning error, although results show that this error is still not competitive with acoustic tracking methods after approximately 10 minutes of descent. Often ocean depth requires hours of descent without GPS or DVL, thus acoustic tracking methods are preferred. This work proposes a solution to localisation in the mid-water column that exploits the fact that current profile layers of water columns are stable over short periods of time (in the scale of minutes). As demonstrated in simulation, using observations of these currents with the ADCP (Acoustic Doppler Current Profiler) mode of the DVL during descent, along with sensor fusion of other low cost sensors, position error growth can be constrained to near the initial velocity uncertainty of the vehicle at the sea surface during a vertical dive. Following DVL bottom-lock, the entire velocity history is constrained to an error similar to the DVL velocity uncertainty. When coupled with a tactical-grade IMU and Time Differenced Carrier Phase (TDCP) GPS measurements, approximately 15 m/hr (2 sigma) position error growth is possible prior to DVL bottom-lock, and 6.5 m/hr (2 sigma) position error growth is possible following DVL bottom-lock. The method is demonstrated using real data from the Sirius AUV coupled with on-bottom view-based SLAM (Simultaneous Localisation and Mapping), without the use of an IMU. Horizontal localisation in the mid-water zone is also explored using an extension to the water-layer framework. The layered water currents are extended to include horizontal gridding, while the ADCP sensor is remodelled to use beam coordinates to exploit horizontal observation. The water current vector field is modelled as correlated spatially through neighbourhood least-squared constraints. Simulations illustrate the performance possible with this method, and results from real data validate this approach. In order to minimize the dead-reckoning error during mid-water zone transits, a novel method to incorporate Inertial Measurements and the constraints of a drag-based vehicle model is outlined. The drag-based Vehicle model uses the water current velocity estimates from the ADCP aiding method, while also accounting for the error from the Vehicle parameters given a prior system identification. Due to the redundant observations of motion from the IMU and DVL when available, there is potential for further improvement in estimates of the Vehicle parameters. Simulations are undertaken to assess the advantage of incorporating a vehicle model, and application on real data from the Sirius AUV validates this method
Mid-water current aided localization for autonomous underwater vehicles
Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Autonomous Robots 40 (2016): 1207–1227, doi:10.1007/s10514-016-9547-3.Survey-class Autonomous Underwater Vehi-
cles (AUVs) typically rely on Doppler Velocity Logs
(DVL) for precision localization near the seafloor. In
cases where the seafloor depth is greater than the DVL
bottom-lock range, localizing between the surface and
the seafloor presents a localization problem since both
GPS and DVL observations are unavailable in the mid-
water column. This work proposes a solution to this
problem that exploits the fact that current profile layers
of the water column are near constant over short time
scales (in the scale of minutes). Using observations of
these currents obtained with the Acoustic Doppler Cur-
rent Profiler (ADCP) mode of the DVL during descent,
along with data from other sensors, the method dis-
cussed herein constrains position error. The method is
validated using field data from the Sirius AUV coupled
with view-based Simultaneous Localization and Map-
ping (SLAM) and on descents up to 3km deep with the
Sentry AUV.This work is supported in part by NCRIS IMOS, the
Australian Research Council (ARC), the New South
Wales Government and the Woods Hole Oceanographic
Institution.2017-02-1
Probablistic approaches for intelligent AUV localisation
This thesis studies the problem of intelligent localisation for an autonomous underwater
vehicle (AUV). After an introduction about robot localisation and specific
issues in the underwater domain, the thesis will focus on passive techniques for AUV
localisation, highlighting experimental results and comparison among different techniques.
Then, it will develop active techniques, which require intelligent decisions
about the steps to undertake in order for the AUV to localise itself. The undertaken
methodology consisted in three stages: theoretical analysis of the problem, tests with
a simulation environment, integration in the robot architecture and field trials. The
conclusions highlight applications and scenarios where the developed techniques have
been successfully used or can be potentially used to enhance the results given by current
techniques. The main contribution of this thesis is in the proposal of an active
localisation module, which is able to determine the best set of action to be executed,
in order to maximise the localisation results, in terms of time and efficiency
Contributions to automated realtime underwater navigation
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater
navigation. These methods may be used in postprocessing with a human in
the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is
on automated approaches that can be used in realtime. The three research threads
are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column,
and iii) model-driven delayed measurement fusion. Contributions to each of
these areas have been demonstrated in simulation, with laboratory data, or in the
field–some have been demonstrated in all three arenas.
The solution to the in situ navigation sensor alignment problem is an asymptotically
stable adaptive identifier formulated using rotors in Geometric Algebra. This
identifier is applied to precisely estimate the unknown alignment between a gyrocompass
and Doppler velocity log, with the goal of improving realtime dead reckoning
navigation. Laboratory and field results show the identifier performs comparably to
previously reported methods using rotation matrices, providing an alignment estimate
that reduces the position residuals between dead reckoning and an external acoustic
positioning system. The Geometric Algebra formulation also encourages a straightforward
interpretation of the identifier as a proportional feedback regulator on the
observable output error. Future applications of the identifier may include alignment
between inertial, visual, and acoustic sensors.
The ability to link the Global Positioning System at the surface to precision dead
reckoning near the seafloor might enable new kinds of missions for autonomous underwater
vehicles. This research introduces a method for dead reckoning through
the water column using water current profile data collected by an onboard acoustic
Doppler current profiler. Overlapping relative current profiles provide information to
simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity
is then integrated to estimate position. The method is applied to field data using
online bin average, weighted least squares, and recursive least squares implementations.
This demonstrates an autonomous navigation link between the surface and the
seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an
interesting challenge. Underwater navigation is a particularly compelling case because
of the relatively long delays inherent in all available position measurements. This research
develops a flexible, model-driven approach to delayed measurement fusion in
realtime Kalman filters. Using a priori estimates of delayed measurements as augmented
states minimizes the computational cost of the delay treatment. Managing
the augmented states with time-varying conditional process and measurement models
ensures the approach works within the proven Kalman filter framework–without
altering the filter structure or requiring any ad-hoc adjustments. The end result is
a mathematically principled treatment of the delay that leads to more consistent estimates
with lower error and uncertainty. Field results from dead reckoning aided
by acoustic positioning systems demonstrate the applicability of this approach to
real-world problems in underwater navigation.I have been financially supported by:
the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered
by the American Society for Engineering Education, the Edwin A. Link
Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic
Programs office
An operational concept for correcting navigation drift during sonar surveys of the seafloor
The accumulation of navigation errors (drift) is a problem in many applications of autonomous underwater vehicles (AUVs), particularly during long-duration underwater surveys. Traditional methods for correcting drift require either surfacing of the vehicle for a global navigation satellite systemupdate or use of an independent acoustic positioning system. These methods may not be desirable or possible due to mission constraints. We propose a solution to this problem completely underwater and without the aid of external navigation systems. The approach is based on an operational concept that uses a modified paired-track survey pattern combined with through-the-sensor navigation corrections from a seafloor imaging sonar. We describe the operational concept, derive a model for its performance limits, validate this model, and demonstrate the concept with real experiments at sea. Using this approach, we provide an opportunity to use either coherent or incoherent through-the-sensor positioning corrections for a mission length increase of only the product of the intratrack spacing and the number of track pairs. We show results from a proof-of-principle experiment using data collected by the 300-kHz synthetic aperture sonar of the NATO Centre for Maritime Research and Experimentation’s Minehunting Unmanned underwater vehicle for Shallow water Covert Littoral Expeditions
Toward autonomous harbor surveillance
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Includes bibliographical references (p. 105-113).In this thesis we address the problem of drift-free navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach uses only onboard sensors in a simultaneous localization and mapping setting and removes the need for any external infrastructure like acoustic beacons. We extract dense features from a forward-looking imaging sonar and apply pair-wise registration between sonar frames. The registrations are combined with onboard velocity, attitude and acceleration sensors to obtain an improved estimate of the vehicle trajectory. In addition, an architecture for a persistent mapping is proposed. With the intention of handling long term operations and repetitive surveillance tasks. The proposed architecture is flexible and supports different types of vehicles and mapping methods. The design of the system is demonstrated with an implementation of some of the key features of the system. In addition, methods for re-localization are considered. Finally, results from several experiments that demonstrate drift-free navigation in various underwater environments are presented.by Hordur Johannsson.S.M
Map building fusing acoustic and visual information using autonomous underwater vehicles
Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 30 (2013): 763–783, doi:10.1002/rob.21473.We present a system for automatically building 3-D maps of underwater terrain fusing
visual data from a single camera with range data from multibeam sonar. The six-degree
of freedom location of the camera relative to the navigation frame is derived as part of the
mapping process, as are the attitude offsets of the multibeam head and the on-board velocity
sensor. The system uses pose graph optimization and the square root information smoothing
and mapping framework to simultaneously solve for the robot’s trajectory, the map, and
the camera location in the robot’s frame. Matched visual features are treated within the
pose graph as images of 3-D landmarks, while multibeam bathymetry submap matches are
used to impose relative pose constraints linking robot poses from distinct tracklines of the
dive trajectory. The navigation and mapping system presented works under a variety of
deployment scenarios, on robots with diverse sensor suites. Results of using the system to
map the structure and appearance of a section of coral reef are presented using data acquired
by the Seabed autonomous underwater vehicle.The work described herein was funded by the National Science Foundation Censsis ERC under grant number
EEC-9986821, and by the National Oceanic and Atmospheric Administration under grant number
NA090AR4320129
Vision-based navigation for autonomous underwater vehicles
This thesis investigates the use of vision sensors in Autonomous Underwater Vehicle (AUV) navigation, which is typically performed using a combination of dead-reckoning and external acoustic positioning systems. Traditional dead-reckoning sensors such els Doppler Velocity Logs (DVLs) or inertial systems are expensive and result in drifting trajectory estimates. Acoustic positioning systems can be used to correct dead-reckoning drift, however they are time consuming to deploy and have a limited range of operation. Occlusion and multipath problems may also occur when a vehicle operates near the seafloor, particularly in environments such as reefs, ridges and canyons, which are the focus of many AUV applications. Vision-based navigation approaches have the potential to improve the availability and performance of AUVs in a wide range of applications. Visual odometry may replace expensive dead-reckoning sensors in small and low-cost vehicles. Using onboard cameras to correct dead-reckoning drift will allow AUVs to navigate accurately over long distances, without the limitations of acoustic positioning systems. This thesis contains three principal contributions. The first is an algorithm to estimate the trajectory of a vehicle by fusing observations from sonar and monocular vision sensors. The second is a stereo-vision motion estimation approach that can be used on its own to provide odometry estimation, or fused with additional sensors in a Simultaneous Localisation And Mapping (SLAM) framework. The third is an efficient SLAM algorithm that uses visual observations to correct drifting trajectory estimates. Results of this work are presented in simulation and using data collected during several deployments of underwater vehicles in coral reef environments. Trajectory estimation is demonstrated for short transects using the sonar and vision fusion and stereo-vision approaches. Navigation over several kilometres is demonstrated using the SLAM algorithm, where stereo-vision is shown to improve the estimated trajectory produced by a DVL
Underwater Robots Part II: Existing Solutions and Open Issues
National audienceThis paper constitutes the second part of a general overview of underwater robotics. The first part is titled: Underwater Robots Part I: current systems and problem pose. The works referenced as (Name*, year) have been already cited on the first part of the paper, and the details of these references can be found in the section 7 of the paper titled Underwater Robots Part I: current systems and problem pose. The mathematical notation used in this paper is defined in section 4 of the paper Underwater Robots Part I: current systems and problem pose
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