4 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
Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.
This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments.
We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd