The Autonomous Redundant Navigation System of an AUV for Mine ...

The Autonomous Redundant Navigation System of an AUV for Mine Counter Measures
Mikael Bliksted Larsen
ATLAS MARIDAN ApS
Agern Allé 3, DK-2970 Hoersholm, Denmark
MBL@maridan.dk, +45 45 76 40 50
Abstract
This paper presents the MARPOSII navigation system of the SEA OTTER MK2 AUV. ATLAS MARIDAN
ApS, Denmark and ATLAS ELEKTRONIK GmbH, Germany are developing SEA OTTER MK2 as a demonstrator
Mine Counter Measures (MCM) AUV for the German Navy. MARPOSII is a state-of-the-art AUV navigation
system, the second in a series of real-time embedded solutions derived from the NAVBOX generic Aided
Inertial Navigation System (AINS) simulation and post-processing tool.
The AINS concept and real-time embedded implementation is briefly described. The main emphasis of the paper
is on the specific MCM AUV functionality: E.g. accurate and fully autonomous navigation with built in redundancy
in algorithms, software and hardware. Capability of "Synthetic Long Baseline" (Synthetic LBL, pat. pending)
and "Simultaneous Localisation And Mapping" (SLAM) systems for bounded long-term covert navigation
and relocation are presented. Systems and concepts are illustrated using data from commercial AUV operations
and recent results from the NATO Underwater Research Centre (NURC) - MX3 AUV mine hunting operation.
1 Introduction
AINS (Aided Inertial Navigation System) is a core
technology behind the successful practical use of
Autonomous Underwater Vehicles (AUV's). AINS
makes use of a powerful Kalman filter to combine
self-contained inertial navigation with measurements
from external (aiding) sensors. Very important synergy
arises from statistically optimum integration of
sensors with complementary characteristics: Automatic
sensor calibration and gyrocompass determination
of geographic heading are natural parts of the
Kalman filter operation. The underlying AINS algorithms
have an inherently modular structure leading
to freedom in choice of vehicle sensor configuration.
Furthermore, the power and flexibility of AINS facilitate
the realisation of unconventional navigation
concepts such as Synthetic LBL 1 and "Simultaneous
Localisation And Mapping" (SLAM) - a practical
demonstration is given using real-world AUV data.
ATLAS MARIDAN has +10 years practical experience
implementing and operating state-of-the-art
autonomous navigation systems. Using the MARPOS
[1] and Synthetic LBL [2, 3] navigation systems,
ATLAS MARIDAN AUVs have successfully completed
>300 commercial survey operations to date.
Customer De Beers Marine, South Africa, is conducting
routine autonomous surveys that consistently
provide < 1.0 meter absolute navigation accuracy.
Typical Doppler-inertial dead-reckoning performance
achieved by their two 2 ATLAS MARIDAN AUVs is
reported to be ~ 1.0 meter/hour.
1 Synthetic Long Baseline (SLBL), pat. pending.
2 De Beers Marine received their second ATLAS
MARIDAN AUV in May 2006.
ATLAS MARIDAN is presently adapting the
MARPOSII navigation system for use in the SEA
OTTER MKII AUV, see Figure 1.
Figure 1 The SEA OTTER MKII AUV for MCM
MARPOSII is derived from the "NAVBOX" AINS
simulation and post-processing tool [4]. Key SEA
OTTER MK2 navigation system features are:
• High performance Doppler-inertial dead
reckoning based navigation.
• Redundant hardware, software and algorithms
- any single point failure is managed.
• Calibration and post-processing package
based on optimal smoothing (option).
• Combined Synthetic LBL [2] and SLAM for
long-term covert autonomous navigation
with bounded error and superior relocation
accuracy (option).

MARPOSII further includes an accurate time system.
Drift is < 0.1s per 24 hours of submerged operation
and proven

Recent applications of NAVBOX include:
• Extremely high performance navigation in
support of Synthetic Aperture Sonar (SAS)
detection of buried mines. German MJ2000
project [9], see also Figure 4.
• NATO Naval Undersea Research Centre
(NURC) MX3 AUV MCM trials, Nov.
2005, La Spezia, Italy.
• NATO harbour protection trials, 2006, La
Spezia.
Figure 4 MJ2000: Synthetic Aperture Sonar
(SAS) for buried mine detection - the first
real-time navigation application derived from
NAVBOX.
Some of the NAVBOX supported AUV sensors and
aiding techniques are:
• IMU
• GPS (or GNSS)
• DVL
• Pressure depth
• USBL
• Fluxgate compass
• ZUPT (Zero velocity update)
• Combined Synthetic LBL and SLAM.
NAVBOX sensor models, simulation and Kalman
filter designs are validated and refined through processing
of large amounts of experimental data. Additional
sensors and navigation techniques are easily
integrated.
2.1 NAVBOX AINS core
NAVBOX includes an accurate implementation of
the AINS algorithms:
• Inertial navigation equations.
• Kalman filter (modular).
• Optimal smoother.
The algorithms are organized as shown in Figure 5.
Each module is explained hereafter.
Figure 5: Aided Inertial Navigation System Block-
Diagram
Inertial navigation is performed by (correct) integration
of measurements from an Inertial Measurement
Unit (IMU). The Kalman filter integrates information
from aiding sensor measurements and applies estimated
navigation error (corrections) to the inertial
navigation.
2.1.1 Inertial Measurement Unit (IMU)
The inertial measurement unit (IMU) consists of orthogonal
triads of gyros and accelerometers. Modern
IMUs generally make use of a strap-down configuration
where the IMU sensors are strapped rigidly onto
the vehicle body - no gimbals. An IMU outputs
change in velocity and change in attitude, often referred
as delta v's (ΔV's) and delta θ's (Δθ's). IMU
data output frequency is typically 100-3000Hz.
IMU's range in performance from low cost MEMS
devices to state-of-the-art Ring Laser Gyro (RLG) or
Fibre Optic Gyro (FOG) based systems.
2.1.2 Inertial Navigation Equations (INE)
The inertial navigation block implements the
strap-down inertial navigation equations: Position,
orientation and velocity are computed by dead reckoning
from initial conditions using Δθ's and ΔV's
output by the IMU. Pure inertial navigation degrades
with time. To maintain accuracy the inertial navigation
block receives corrections computed by the
Kalman filter. This configuration is referred to as

tightly coupled or closed loop. The inertial navigation
equations make use of accurate Earth models
(WGS84) and special models for Earths gravity.
2.1.3 AINS Kalman filter
The error state Kalman filter (ESKF) processes information
from aiding sensors. The ESKF estimates
errors in inertial navigation (position, attitude/heading
and velocity) and important errors in the
IMU and aiding sensors. Feedback correction of the
inertial navigation is beneficial because it prevents
linearization errors within the Kalman filter from becoming
significant. Feedback is particularly important
when low cost inertial sensors are used (=> large
errors). Many advanced navigation concepts can be
implemented efficiently via "shrewd" augmentation
of the Kalman filter.
2.1.4 Sensor models
The Kalman filter makes use of sensor models to extract
and optimally weight measurement information:
Differences between the expected (sensor model) and
the measured values are used by the Kalman filter to
improve its estimate of errors. NAVBOX sensor configuration
and parameter values are user configurable.
2.1.5 Optimal smoothing
Optimal smoothing [6] is a Kalman filter based postprocessing
technique that provides statistically optimum
results 3 . Optimal smoothing uses all past and
future sensor measurements to compute the optimum
navigation solution for every time point of a mission
(fixed interval smoothing). Note, that optimal
smoothing does NOT low-pass filter or in any other
way degrade the precise high dynamics capability of
inertial navigation. The practical benefit of optimal
smoothing is substantial as shown in section 3. In
support of rapid data evaluation, innovative concepts
are in place to reduce post-processing time to an insignificantly
small percentage of mission time.
2.1.6 Synthetic LBL
Figure 6 illustrate the Synthetic LBL concept [2].
Through change in position and a dead-reckoning capability,
the AUV creates synthetic baselines, which
can be used for trilateration of acoustic range
measurements from a single beacon, see Figure 6
(top). In principle, this is much the same as range
trilateration in conventional LBL, see Figure 6 (bottom).
NAVBOX uses the power and flexibility of its
AINS Kalman filter to implement the Synthetic LBL
concept: The state vector is augmented with feature
(beacon) position and initial feature (beacon) position
uncertainty is managed by assigning proper values to
elements of the Kalman covariance matrix. This arrangement
is referred to as a stochastic map in the
robotics navigation literature. The Kalman filter will
3 Certain statistical preconditions apply.
"map" the feature (beacon) as the AUV moves
around collecting range measurements and since the
Kalman filter knows that the feature is stationary, the
measurements will simultaneously prevent further
growth in position error. A practical example using
real-world experimental AUV data is given in section
3.
Figure 6 Illustration of the Synthetic LBL concept:
Duality between single beacon ranging
combined with dead-reckoning (topmost figure)
and conventional LBL (bottom).
2.1.7 SLAM
Terminology used in the previous section suggests
commonality between SLAM and Synthetic LBL. In
fact, NAVBOX implements Synthetic LBL and
SLAM as a simple common extension to the
NAVBOX AINS Kalman filter. The only principal
difference is the nature of the processed observations:
Range observations for artificial beacons vs. vehicle
relative feature position for natural seabed features.
Using Kalman terminology, this is a simple matter of
using different observation models dependent on type
of observation. Notice that possible prior knowledge
of seabed feature position (e.g. a map) may be utilized
via state vector and covariance matrix initialization.
A practical example using real-world experimental
AUV data is given in section 3.

3 NAVBOX processing of real AUV data.
This section will demonstrate and discuss selected
NAVBOX capabilities by (offline) processing of previously
recorded real-world AUV data:
• Doppler-inertial dead reckoning
• Synthetic LBL
• SLAM
• Optimal smoothing
3.1 ATLAS MARIDAN M62 AUV test in
Skagerak, 28. September 2001
Figure 7 depicts the ATLAS MARIDAN M62 AUV
vehicle trajectory during testing in Skagerak between
Denmark and Norway (58°N) in 2001. Total mission
time was about 140 minutes. The high-quality reference
was a Sonardyne Ltd., UK, LBL acoustic positioning
system accurate to about 3 meters absolute
and ~0.2 meter relative.
Figure 7: ATLAS MARIDAN M62 AUV operation
and Trajectory, Skagerak 28. September 2001
Only the seabed part of the data set is used (see
Figure 8). Thus, the LBL reference is available at all
times and provides a single consistent reference. For
the sole purpose of this demonstration the reference
is considered accurate ~0.2m.
Lat (relative) [m]
-100
-200
-300
-400
3.2 Navigation sequence
The following navigation sequence is common to the
examples hereafter:
1. Alignment: 5 minute rapid deployment gyrocompass
alignment (using LBL position
reference as GPS substitute).
2. Dive: 3 minute free inertial dive section -
equivalent to a 600 meter flank speed dive
of a SEA OTTER MK2 equivalent AUV.
3. Site survey: 2-hour fully autonomous highresolution
site survey.
4. Surfacing: 4 minute free inertial surfacing
(inclusive of ~1 minute for "GPS" reacquisition).
5. Post-mission alignment: 2 minutes of
post-mission gyrocompass alignment (using
LBL position reference as GPS substitute).
The site survey part of the mission is performed using
different navigation strategies:
1. Standalone Doppler-inertial
dead-reckoning.
2. SLAM using three natural seabed features
3. Synthetic LBL using a single beacon with
unknown but stationary position (range-only
based SLAM).
Used colours match plots and figures hereafter.
Figure 8 show the four beacon (transponder) LBL
array used as reference (black/red diamonds), the
beacon in the centre (red diamond) is used for Synthetic
LBL navigation, and three natural features
(green triangles) are used for SLAM.
600
500
400
300
200 Acoustic transponder (reference)
100
0
Vehicle trajectory relative Lat: 57.9247384 Lon: 9.49193279
End of trajectory
Natural seabed feature (SLAM)
Beacon (Synthetic LBL)
Start of trajectory
-800 -600 -400 -200 0 200 400 600
Lon (relative) [m]
Figure 8 Trajectory, reference transponders,
natural seabed features and Synthetic LBL beacon.

3.3 Navigation results
Figure 9 hereunder show the NAVBOX results. The
two topmost time series are "real-time" navigation
and the lower time series come from post-processing
by optimal smoothing. Y-axis: "Radial position error"
is the difference between NAVBOX AINS and
the LBL acoustic reference. Y-axis "Quality CEP50 4
[m]" is the Kalman filter / smoother's estimate of position
accuracy. Integrity of the "real-time" navigation
is confirmed since measured error is quite consistent
with expected CEP50 (dotted lines).
"Real-time" AINS
Optimal smoothing
Figure 9 NAVBOX results, ATLAS MARIDAN
M62 Skagerak 28. September 2001
Several things deserve commenting. The discussion
will assume knowledge of Kalman filter terminology.
"Real-time" AINS:
Navigation error grows during the dive phase
(t =5-8min). When DVL velocity becomes available,
much of the dive phase error is recovered - this is due
to correlation between velocity error and position er-
4 CEP50 - Circular Error Probable at 50%, e.g. there
is a 50% chance true position is within circle of this
radius.
ror maintained within the Kalman covariance matrix
[6].
As expected, there is no difference between SLAM
and dead-reckoning (DR) navigation until t ~ 115
minutes, when the AUV revisits the three seabed features
(see Figure 8). Passing the features a second
time completely removes error accrued during the
site survey and reduce CEP by ~50% to just over 1
meter - this is consistent with the actual position error
(

general provide approximately the same
post-processed accuracy for this type of mission.
However, the example is a perfect illustration of the
superior integrity of Synthetic LBL - particularly the
ability to manage degraded dead-reckoning performance
in a robust manor.
3.4 NATO Undersea Research Center (NURC)
MX3 AUV Mine Hunting Exercise.
ATLAS MARIDAN participated in NURC-MX3 trials,
La Spezia, Italy, Nov. 2005 operating the SEA
OTTER MK1 (M600) AUV from the research vessel
"Leonardo", see Figure 10.
Figure 10 NURC-MX3 AUV Mine Hunting Exercise.
Above: NURC vessel "Leonardo".
Below: MBE mosaic of trial area using
post-processed AUV navigation data.
The MBE mosaic above was generated by De Beers
Marine using post-processed AUV navigation data.
The mosaic is composed of data from several AUV
tracks. No navigation errors are visible despite very
high resolution.
Figure 11 shows the trajectory of a multibeam echosounder
(MBE) "super" classification run. Mission
duration was 1.5 hours travelling at about 2.0 m/s.
Depth ranges from 15 to 40 m and the distance from
start to end location is approximately 2.5 km.
Figure 11 Multi Beam Echosounder (MBE)
"super" classification run - 9 potential targets in
percentage clearance trial area revisited.
Figure 12 shows NAVBOX estimated position accuracy.
CEP50 at the end of the real-time run is expected
to be ~2.5m. Using optimal smoothing the accuracy
is improved by at least a factor of 2 to less
than 1 m. This example illustrates the efficiency of
smoothing when applied to linear trajectories with
position updates (GPS) at both ends of the mission.
Figure 12 Positioning accuracy
Further details of recent ATLAS MARIDAN AUV
operations are given in [8].

4 MARPOSII navigation system of the SEA
OTTER MK2 AUV.
MARPOSII, the successor of MARPOS [1], is the
real-time embedded equivalent of the offline
NAVBOX tool. Key features are:
• High performance Doppler-inertial dead
reckoning based navigation.
• Redundant hardware, software and algorithms
- any single point failure is managed.
• Calibration and post-processing package
based on optimal smoothing (option).
• Combined Synthetic LBL [2] and SLAM for
long-term covert autonomous navigation
with bounded error and superior relocation
accuracy (option).
4.1 Hardware configuration
• Navigation grade IMU (CEPR < 1NMPH)
• DVL: Teledyne RDI Workhorse navigator
• Spread spectrum (wideband) acoustic transceiver:
Sonardyne Ltd. AVTRAK MK2
• Redundancy: Processors, interfaces, power
supply, sensors and algorithms.
• GPS, CTD / SVS, fluxgate, pressure sensor
• OCXO based time system.
Drift < 0.1 second per 24 hours, supported
by GPS 1PPS.
• High performance, low power floating point
processor, Figure 13
Figure 13 Redundant PowerPC based floatingpoint
processors
It is planned to use the ATLAS MARIDAN SEA
OTTER MK1 (M600) AUV (Figure 14) owned and
operated by WTD71 in Eckerförde, Germany for further
MARPOSII development and experimental validation.
Figure 14: M63 M600 Type AUV Operated by
WTD71, Eckernförde, Germany
4.2 Software configuration
• Real-time Kernel.
• Dual processor boards - dual navigation
software packages.
• Flexible messaging system and CORBA [7]
support.
5 Summary
The power and versatility of AINS and the
NAVBOX simulation and post-processing tool were
described. Recorded AUV navigation data were used
to give a practical demonstration and detailed discussion
of the two "unconventional" navigation concepts
"Synthetic LBL" and SLAM. The Synthetic LBL
concept was found to provide superior integrity and
very good autonomous navigation performance. The
efficiency of optimal smoothing for offline improvement
of navigation accuracy was shown and key features
of the MARPOSII real-time embedded navigation
system were listed.
The solid and versatile basis provided by NAVBOX
and MARPOSII means that present and future navigation
system requirements will be met.
6 Acknowledgments
The NAVBOX AINS simulation and post-processing
tool was developed largely within the AUV 2000
project of the German Federal Office of Defence
Technology and Procurement (BWB).
The author wishes to thank past and present colleagues
for their contributions, first and foremost
Morten Soede Nielsen and Per Fogt Nielsen of
ATLAS MARIDAN and Ursula Hölscher-Höbing of
ATLAS Elektronik.
The author would also like to thank the skilled members
of the DeBeers Marine AUV team for rewarding
co-operation.

7 Literature
[1] Mikael Bliksted Larsen, High Performance Doppler-Inertial
Navigation - Experimental Results,
In proceedings of IEEE Oceans, 2000.
[2] Mikael Bliksted Larsen, Synthetic Long Baseline
Navigation of Underwater Vehicles, In proceedings
of IEEE Oceans, 2000.
[3] Mikael Bliksted Larsen, Methods And Systems
For Navigating Under Water, patent application
PCT/DK01/00141. 2000/2001.
[4] Mikael B. Larsen, Ursula Hölscher-Höbing,
Aided Inertial Navigation System Solutions for a
Family of AUV's and Advanced Underwater Vehicles,
UDT 2005, Amsterdam.
[5] www.mathworks.com
[6] R.G. Brown and P. Y. C. Hwang, Introduction to
Random Signals and Applied Kalman Filtering,
Wiley; 3 edition (November 14, 1996), ISBN:
0471128392
[7] CORBA: http://www.omg.org
[8] Tronje Schneider-Pungs, Sea Trials with the
Autonomous Underwater Vehicle Sea Otter, proceedings
of UDT Europe 2006, Hamburg, Germany.
[9] Ursula Hölscher-Höbing, Mikael B. Larsen,
Navigation systems solutions: Application for a
MCM System Consisting of Guidance Vessel -
Surface Drone - Remote Underwater vehicle,
proceedings of UDT Europe 2006, Hamburg,
Germany.
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