This document discusses localizing an underwater vehicle using an IMU and laser vision system (LVS). It proposes fusing an IMU, which provides acceleration and velocity data but accumulates errors over time, with an LVS, which can provide position vectors but has limited range. A Kalman filter is used to fuse the IMU and LVS data, providing optimal position and velocity estimates. Experiments are conducted using the system for teleoperating and closed-loop position control of a remotely operated underwater vehicle.

1. Localization of underwater vehicle using IMU and LVS
Umair Ali
18016522-008
19-Sept-2019

2. Imu Sensor (Common Sensor)
• shape
• dead recking sensor
• roburst and high frequency
data
• error accumulation with
time
• typical sensors(DVL,
USBL) can correct
accumulated error
figure: imu sensor shape
figure: block diagram of imu sensor

3. Why to use cameras and Laser pointers
• typical sensors are good
enough for shallow water
• typical sensors has lack of
percision in underwater
environment
• cameras and Laser
pointers have higher
frequency than acoustic
but limited range with
accurate results.
figure: inspection of ship hull
figure: laser vision system

4. previous work with IMU and LVS
• integration of IMU and
GPS
• Integration of IMU and
DVL
• methodologies are made
using LVS to calibrate yaw
and correct parpendicular
distances.
figure: laser plane image to be seen by camera
figure: laser pointer image

5. Purpose of this research work
• propose velocity and
position vector
• LVS gives position vector
of vehicle refered to target
• Imu gives acceleration and
velocity of vehicle
• LVS and Imu are fused in
KF based system
figure: position, vilocity and acceleration vector
figure: Kalman Filter diagram

6. Benefits of using KF for fusion of Data
• MKF produces optimal
position vector as input for
closed loop position
control system
• MKF produces position
and velocity vector even if
there is no input from LVS
for 10 seconds
figure: closed loop position control system
figure: position estimation reference

7. Methodology
• xyz are for position and
others are for orientation in
n vector
• uvw are linear velocities
and pqr are angular
velocities
• angles and angular
velocities agains x and y
are stable because of
vehicle.
figure: position vector of vehicle
figure: velocity vector of vehicle
figure: x position and psi orientation of vehicle

8. Methodology - LVS
• 2 laser pointers and 1
charged copupled device
CCD camera
• position of x axis and
orientation of z axis is
achieved by L1 L2 and psi.
• mapping is achieved by
polynomial because
triangular mapping has
hardware constraints
polynomial plot
triangular mapping

9. Methodology - LVS - Tracking of Target
• active contour(snake)
vision (arround object)
• features are lines and
edges
• object is selected
fig: active contour
fig: snake curves
figL sx and sy are center of target, xo yo are center of
image ax, ay the camera focal lengths for xy image axis

10. Methodology - IMU used here
• 3 accelerometer and 3
gyroscopes
• strap down configuration
(fixed)
• practice of mathematical
model leads to unbound
position error of IMU
• external sensor is needed
for IMU

11. Methodology - LVS/IMU based MSKF
• in linear stationary model x
is state vector and w is
white noise and F is state
transition metrix
• measurement of N sensor
can be done by this
equation
• i sensors and k values of
each sensor

12. Methodology - LVS/IMU based MSKF
• Estimation stage of
kalman filter where K is
kalman gain for i sensor
while P is uncertainty
• Prediction stage is defined
as

13. Methodology - LVS/IMU based MSKF- Wiener
process acceleration model (stochastic)
• xk(state vector) with first 9
states of position velocity
and acceleration and last
two states tells angle and
angular velocity around z
axis

14. Methodology - LVS/IMU based MSKF- Wiener
process acceleration model (stochastic)
• data fusion decision
• target refer frame position
and yaw can find out from
LVS
•

15. Methodology - LVS/IMU based MSKF- Wiener
process acceleration model (stochastic)
• IMU is main sensor while
LVS is external sensor
• If LVS not available then
only IMU
• othervise

16. Experiments
• 1) teleoperation using a
joystick
• 2) closed loop position
control system
• Experiment is operated in
a pool

17. Exprements - system components
• module
• control system
• joystick
• laser pointer
• CCD camera
• IMU
• Aluminium object

18. Experiment - 1 teleoperation scenerio
• ROV is teleoperated by
joystick
• while user detects object
kalman filter fuse LVS and
IMU

19. Experiment - 2 Closed Loop Position Control
Scenario
• To derive the vehicle on
desired location
• A motion controller is
implemented by kinematic
controller on xy and PD
controller on z
• a is difference of principle
axis and distant vector e

20. conclusion
• fuse IMU and LVS as
position sensor
• LVS is external sensor of
IMU
• Proposed method can be
used for closed loop
position control as it
provide smooth
measurement at high
frequency