Eren, Firat

Pe\u27eri, Shachak

Rzhanov, Yuri

Thein, May-Win

English

University of New Hampshire

University of New Hampshire
University of New Hampshire Scholars' Repository
Center for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping
4-2014
An image processing approach for determining of
relative pose of unmanned underwater vehicles
Yuri Rzhanov
University of New Hampshire, Durham, Yuri.Rzhanov@unh.edu
Firat Eren
University of New Hampshire, Durham, Firat.Eren@unh.edu
May-Win Thein
University of New Hampshire, Durham
Shachak Pe'eri
University of New Hampshire, Durham, shachak.peeri@unh.edu
Follow this and additional works at: https://scholars.unh.edu/ccom
This Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire
Scholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New
Hampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.
Recommended Citation
Y. Rzhanov, Eren, F., Pe'eri, S., and Thein, M. - W., “An image processing approach for determining of relative pose of unmanned
underwater vehicles”, Oceans'14 MTS/IEEE. Taipei, Taiwan, 2014.
An image processing approach for determining of relative pose of unmanned underwater vehicles 
Yuri Rzhanov, Firat Eren, May-Win Thein and Shachak Pe'eri 
The use of a light source as a beacon is advantageous for the guidance and control of Unmanned 
Underwater Vehicles (UUVs). This approach allows a follower UUV to determine its relative pose 
(position and orientation) using low-cost commercial off the shelf (COTS) hardware (e.g., metal halide 
light sources). In order to design an effective detector unit for the follower UUV and predict its 
performance, a simulator program has been developed.  The program simulates a light field using 
hardware and environmental parameters describing the light source, water properties, the detector unit 
geometry and electronic sensitivity. The simulator allows examination of different 3D detector array 
shapes of varying sizes (physical dimensions and number of detectors). It is convenient to present 
simulator output as an image, where each pixel represents the intensity logged by a corresponding 
detector. These image outputs are evaluated for the development of control algorithms for UUVs.  
Currently control algorithms assume that the water column is uniform with a systematic background 
noise. Considered control algorithms are able to provide guidance based on relative intensity values, 
where the light field samples on the detector array resembles a Gaussian beam pattern. However, 
disturbances in the medium (e.g., sediment plume) may cause non-uniform distribution of the scatterers 
that can distort the beam pattern. As a result, the control algorithms could misinterpret the acquired 
image and direct the follower UUV away from the guiding beam. The probability for such a situation 
increases with distance as the beam diverges. This paper suggests an alternative approach for the 
development of UUV control algorithms using calculations of various moments of the image (e.g. local 
Hessian estimations). This method allows the evaluation of the array performance with different array 
geometries and a varying number of detector elements.  
The reference coordinate system assumes that the light source is at the origin, the X-axis is coincident 
with the center of the guiding beam, and the Y-axis is parallel to the rows of the detector array in the 
initial follower UUV position. The Z-axis is orthogonal to the XY plane, thus completing the orthogonal 
triad. The motion of the follower UUV can be described as a 5 degree of freedom (DOF) pose with 
respect to the light source: three translational motions (i.e., X, Y, and Z-displacements) and two 
rotations (yaw rotation about the Y-axis and pitch rotation about the Z-axis). Simulations have shown 
that it is not possible to decouple roll (rotation about the X-axis) from yaw or pitch because of the 
symmetry of the light beam. Instead, it is assumed that the follower has a stabilizer that maintains a 
fixed roll angle.  
The location of the peak beam value,(𝑦�, 𝑧�) is determined with sub-pixel accuracy and is assumed to be 
within the array, where 𝑦�  and 𝑧� are the row and line coordinates for a given detector in the array, 
respectively.  The moments are defined as the weighted sum of the intensity values of the array 
detectors,𝐼�,� with respect to(𝑦�, 𝑧�): 
𝑀�� = ∑ (𝑦� − 𝑦�)��,� (𝑧� − 𝑧�)�𝐼�,� (1) 
 
It was demonstrated that it is sufficient to calculate only 5 moments for a 5 DOF pose, specifically, 
𝑀��,𝑀��,𝑀��,𝑀��,𝑀��. Specific functions of moments are chosen for each particular array geometry. 
Preliminary results show good performance for different array geometries. Relative pose determination 
is robust with respect to noise and number of detectors in the array. The performance evaluation of 
planar arrays shows that algorithm performance degrades with distance. The performance evaluation of 
curved arrays shows a relationship between the light field structure and the curvature of the array, in 
addition to distance. Image distortions that represent variable turbidity in water are also to be 
evaluated. 


An image processing approach for determining of relative pose of unmanned underwater vehicles

UNH Scholars' Repository

University of New HampshireUniversity of New Hampshire Scholars' RepositoryCenter for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping4-2014An image processing approach for determining ofrelative pose of unmanned underwater vehiclesYuri RzhanovUniversity of New Hampshire, Durham, Yuri.Rzhanov@unh.eduFirat ErenUniversity of New Hampshire, Durham, Firat.Eren@unh.eduMay-Win TheinUniversity of New Hampshire, DurhamShachak Pe'eriUniversity of New Hampshire, Durham, shachak.peeri@unh.eduFollow this and additional works at: https://scholars.unh.edu/ccomThis Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New HampshireScholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of NewHampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.Recommended CitationY. Rzhanov, Eren, F., Pe'eri, S., and Thein, M. - W., “An image processing approach for determining of relative pose of unmannedunderwater vehicles”, Oceans'14 MTS/IEEE. Taipei, Taiwan, 2014.An image processing approach for determining of relative pose of unmanned underwater vehicles Yuri Rzhanov, Firat Eren, May-Win Thein and Shachak Pe'eri The use of a light source as a beacon is advantageous for the guidance and control of Unmanned Underwater Vehicles (UUVs). This approach allows a follower UUV to determine its relative pose (position and orientation) using low-cost commercial off the shelf (COTS) hardware (e.g., metal halide light sources). In order to design an effective detector unit for the follower UUV and predict its performance, a simulator program has been developed.  The program simulates a light field using hardware and environmental parameters describing the light source, water properties, the detector unit geometry and electronic sensitivity. The simulator allows examination of different 3D detector array shapes of varying sizes (physical dimensions and number of detectors). It is convenient to present simulator output as an image, where each pixel represents the intensity logged by a corresponding detector. These image outputs are evaluated for the development of control algorithms for UUVs.  Currently control algorithms assume that the water column is uniform with a systematic background noise. Considered control algorithms are able to provide guidance based on relative intensity values, where the light field samples on the detector array resembles a Gaussian beam pattern. However, disturbances in the medium (e.g., sediment plume) may cause non-uniform distribution of the scatterers that can distort the beam pattern. As a result, the control algorithms could misinterpret the acquired image and direct the follower UUV away from the guiding beam. The probability for such a situation increases with distance as the beam diverges. This paper suggests an alternative approach for the development of UUV control algorithms using calculations of various moments of the image (e.g. local Hessian estimations). This method allows the evaluation of the array performance with different array geometries and a varying number of detector elements.  The reference coordinate system assumes that the light source is at the origin, the X-axis is coincident with the center of the guiding beam, and the Y-axis is parallel to the rows of the detector array in the initial follower UUV position. The Z-axis is orthogonal to the XY plane, thus completing the orthogonal triad. The motion of the follower UUV can be described as a 5 degree of freedom (DOF) pose with respect to the light source: three translational motions (i.e., X, Y, and Z-displacements) and two rotations (yaw rotation about the Y-axis and pitch rotation about the Z-axis). Simulations have shown that it is not possible to decouple roll (rotation about the X-axis) from yaw or pitch because of the symmetry of the light beam. Instead, it is assumed that the follower has a stabilizer that maintains a fixed roll angle.  The location of the peak beam value,(𝑦�, 𝑧�) is determined with sub-pixel accuracy and is assumed to be within the array, where 𝑦�  and 𝑧� are the row and line coordinates for a given detector in the array, respectively.  The moments are defined as the weighted sum of the intensity values of the array detectors,𝐼�,� with respect to(𝑦�, 𝑧�): 𝑀�� = ∑ (𝑦� − 𝑦�)��,� (𝑧� − 𝑧�)�𝐼�,� (1)  It was demonstrated that it is sufficient to calculate only 5 moments for a 5 DOF pose, specifically, 𝑀��,𝑀��,𝑀��,𝑀��,𝑀��. Specific functions of moments are chosen for each particular array geometry. Preliminary results show good performance for different array geometries. Relative pose determination is robust with respect to noise and number of detectors in the array. The performance evaluation of planar arrays shows that algorithm performance degrades with distance. The performance evaluation of curved arrays shows a relationship between the light field structure and the curvature of the array, in addition to distance. Image distortions that represent variable turbidity in water are also to be evaluated. 

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An image processing approach for determining of relative pose of unmanned underwater vehicles

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