614 research outputs found
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 INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE
The work in this thesis concerns with the development of a novel multisensor data fusion
(MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic
and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous
underwater vehicle (AUV) navigation system, formed by an integration of global positioning
system and inertial navigation system (GPS/INS).
The Kalman filter has been a popular method for integrating the data produced
by the GPS and INS to provide optimal estimates of AUVs position and attitude. In
this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is
proposed. The former is used to fuse the data from a variety of INS sensors whose
output is used as an input to the later where integration with GPS data takes place.
The use of an adaptation scheme based on fuzzy logic approaches to cope with the
divergence problem caused by the insufficiently known a priori filter statistics is also
explored. The choice of fuzzy membership functions for the adaptation scheme is first
carried out using a heuristic approach. Single objective and multiobjective genetic
algorithm techniques are then used to optimize the parameters of the membership
functions with respect to a certain performance criteria in order to improve the overall
accuracy of the integrated navigation system. Results are presented that show
that the proposed algorithms can provide a significant improvement in the overall
navigation performance of an autonomous underwater vehicle navigation.
The proposed technique is known to be the first method used in relation to AUV
navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd.,
Qinetiq, Subsea 7 and South West Water PL
Towards a robust slam framework for resilient AUV navigation
Autonomous Underwater Vehicles (AUVs) are playing an increasing part in modern
navies, to the point that the control of oceans will soon be decided by their strategic
use. In face of more complex missions occurring in potentially hostile environments,
the resilience of such systems becomes critical. In this study, we investigate the
following scenario: how does a lone AUV could recover from a temporary breakdown
that has created a gap in its measurements, while remaining beneath the surface to
avoid detection? It is assumed that the AUV is equipped with an active sonar and
is operating in an uncharted area. The vehicle has to rely on itself by recovering
its location using a Simultaneous Localization and Mapping (SLAM) algorithm.
While SLAM is widely investigated and developed in the case of aerial and terrestrial
robotics, the nature of the poorly structured underwater environment dramatically
challenges its effectiveness. To address such a complex problem, the usual side
scan sonar data association techniques are investigated under a global registration
problem while applying robust graph SLAM modelling. In particular, ways to
improve the global detection of features from sonar mosaic region patches that react
well to the MICR similarity measure are discussed. The main contribution of this
study is centered on a novel data processing framework that is able to generate
different graph topologies using robust SLAM techniques. One of its advantages is to
facilitate the testing of different modelling hypotheses to tackle the data gap following
the temporary breakdown and make the most of the limited available information.
Several research perspectives related to this framework are discussed. Notably, the
possibility to further extend the proposed framework to heterogeneous datasets and
the opportunity to accelerate the recovery process by inferring information about
the breakdown using machine learning.PH
Toward lifelong visual localization and mapping
Thesis (Ph.D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-181).Mobile robotic systems operating over long durations require algorithms that are robust and scale efficiently over time as sensor information is continually collected. For mobile robots one of the fundamental problems is navigation; which requires the robot to have a map of its environment, so it can plan its path and execute it. Having the robot use its perception sensors to do simultaneous localization and mapping (SLAM) is beneficial for a fully autonomous system. Extending the time horizon of operations poses problems to current SLAM algorithms, both in terms of robustness and temporal scalability. To address this problem we propose a reduced pose graph model that significantly reduces the complexity of the full pose graph model. Additionally we develop a SLAM system using two different sensor modalities: imaging sonars for underwater navigation and vision based SLAM for terrestrial applications. Underwater navigation is one application domain that benefits from SLAM, where access to a global positioning system (GPS) is not possible. In this thesis we present SLAM systems for two underwater applications. First, we describe our implementation of real-time imaging-sonar aided navigation applied to in-situ autonomous ship hull inspection using the hovering autonomous underwater vehicle (HAUV). In addition we present an architecture that enables the fusion of information from both a sonar and a camera system. The system is evaluated using data collected during experiments on SS Curtiss and USCGC Seneca. Second, we develop a feature-based navigation system supporting multi-session mapping, and provide an algorithm for re-localizing the vehicle between missions. In addition we present a method for managing the complexity of the estimation problem as new information is received. The system is demonstrated using data collected with a REMUS vehicle equipped with a BlueView forward-looking sonar. The model we use for mapping builds on the pose graph representation which has been shown to be an efficient and accurate approach to SLAM. One of the problems with the pose graph formulation is that the state space continuously grows as more information is acquired. To address this problem we propose the reduced pose graph (RPG) model which partitions the space to be mapped and uses the partitions to reduce the number of poses used for estimation. To evaluate our approach, we present results using an online binocular and RGB-Depth visual SLAM system that uses place recognition both for robustness and multi-session operation. Additionally, to enable large-scale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate long-term mapping using approximately nine hours of data collected in the MIT Stata Center over the course of six months. Ground truth, derived by aligning laser scans to existing floor plans, is used to evaluate the global accuracy of the system. Our results illustrate the capability of our visual SLAM system to map a large scale environment over an extended period of time.by Hordur Johannsson.Ph.D
- …