OPTO-ACOUSTIC DATA FUSION FOR SUPPORTING THE GUIDANCE OF
REMOTELY OPERATED UNDERWATER VEHICLES (ROVs)
F. Bruno1, A. Lagudi1, G. Ritacco1, M. Muzzupappa1, R. Guida2
1 Dept. of Mechanical, Energetics and Management Engineering, Università della Calabria, Rende (CS), Italy – (fabio.bruno, antonio.lagudi, gerardo.ritacco, maurizio.muzzupappa)@unical.it
2
Whitehead Sistemi Subacquei S.p.A., Pozzuoli (NA), Italy – ramona.guida@wass.it
Commission V
KEY WORDS: 3D opto-acoustic camera, optical and acoustic data fusion, stereovision system, system calibration, ROV guidance
ABSTRACT:
Remotely Operated underwater Vehicles (ROVs) play an important role in a number of operations conducted in shallow and deep water (e.g.: exploration, survey, intervention, etc.), in several application fields like marine science, offshore construction, and underwater archeology. ROVs are usually equipped with different imaging devices, both optical and acoustic. Optical sensors are able to generate better images in close range and clear water conditions, while acoustic systems are usually employed in long range acquisitions and do not suffer from the presence of turbidity, a well-known cause of coarser resolution and harder data extraction. In this work we describe the preliminary steps in the development of an opto-acoustic camera able to provide an on-line 3D reconstruction of the acquired scene. Taking full advantage of the benefits arising from the opto-acoustic data fusion techniques, the system was conceived as a support tool for ROV operators during the navigation in turbid waters, or in operations conducted by means of mechanical manipulators.
The paper presents an overview of the device, an ad-hoc methodology for the extrinsic calibration of the system and a custom software developed to control the opto-acoustic camera and supply the operator with visual information.
1. INTRODUCTION
During the last few years, there has been growing interest for the development of efficient methodologies and systems in the underwater research area, in order to deal with the challenging problems of monitoring, survey and data gathering.
In the field of cultural heritage,
the scientific community and the international Cultural Heritage safeguarding bodies have established the need to promote, protect, and preserve, possibly in-situ, the submerged artefacts and sites.
The CoMAS project, started in 2011, aims to develop new materials and technologies for supporting the restoration, conservation and documentation of underwater cultural heritage. The project goal is the definition of a conservation methodology that includes several stages: documentation, cleaning, restoration, maintenance and monitoring.
One of the most challenging aspects of this project is the set-up of a special ROV (Remotely Operated Vehicle) devoted to the monitoring, routine cleaning (through the use of a custom mechanical manipulator), and 3D mapping of the submerged archeological structures. These specific tasks require the accurate localization of the vehicle within the operational environment and, above all, a clear representation of the underwater scene in presence of low visibility conditions. ROVs are usually equipped with different imaging devices both optical and acoustic. The acoustic systems typically give good results in long-range acquisition and do not suffer from the water turbidity, but the resulting data are affected by low resolution and accuracy. The optical systems, in contrast, are more suited for close-range acquisitions and allow for gathering high-resolution data and target details, but the results are constrained by a limited visibility range.
Hence, the fusion of data captured by these two types of systems stands as a promising technique in underwater applications, as it allows for compensating their respective limitations. Since the two categories of sensors are based on different physical principles, they provide, in general, different information of the scene to be acquired, and different methods
are employed to process the data. Therefore, the integration of the two types of data gathered from these sensors is a very promising and interesting field that calls for new solutions. Despite the difficulty of combining two modalities that operate at different resolutions, technological innovation and advances in acoustic sensors have progressively allowed for the generation of good-quality high-resolution data suitable for integration, and the related design of new techniques and systems for underwater scene reconstruction.
The aim of this work is to describe the preliminary steps in the development of an opto-acoustic camera able to provide on-line 3D reconstructions. Taking full advantage of the benefits arising from the opto-acoustic data fusion techniques, the system was conceived as a support tool for ROV operators during the navigation in turbid waters or in operation conducted by means of mechanical manipulators. The operator will be able to choose the most suitable visualization technique (optical, acoustic) according to the working conditions or, if needed, to fuse both of them in a single image where the missing parts of the acquired optical data are covered by the acoustic acquisitions. The remainder of this paper is organized as follows: Section
2
presents the state-of-the-art concerning the integration of optical and acoustic sensors in the field of underwater applications, and the solutions adopted to fuse the data. Section 3 deals with the system configuration and the hardware specifications of the sensors that constitute the opto-acoustic camera. In Section 4 we formalize the extrinsic calibration problem of the system and provide a methodology to solve it. The conclusive section presents the main features and a first prototype of the user interface.
2. RELATED WORKS
community, so various areas image process In the field of merge optical 2000). In this merge optica (acquired thr resolution qua fusion is achie across the two warp, by warp by the same co A system for terrain through camera and ra (Kunz and S optimization, framework (√ trajectory, the frame. Matche as images of submap match by linking R trajectory. The the ROV’s tra sensors, and th observations matches, in calibration of position and specialized no A further exa acoustic senso work focused large area ma acoustic sens understanding during the na obtained from (Coda Echosc data with resp After some pre with respect to in this case) u (Besl et al., 19 means of an a the poses of observed obje and the acous use positioning Although ver drawback of r the calibration linking the p domain and a Finally, we ha stereo reconstr which aims to composed by order to impr waters. Due to acoustic syste the two views in the acoustic image lies on point in the op
o that data fusio such as sens sing (Khaleghi e f underwater app and acoustic s work, simple fi al photomosaic rough a penci antitative opto-a eved by manual o representation ping the photom orresponding po
automatically h the fusion o ange data from
Singh, 2013). square root inf √SAM) to simu e map, and th ed visual featur f 3D landmar hes are used to i ROV poses from
e system is ini ajectory, as me
hen adds facto and constraint an incrementa
the navigation orientation i odes in the pose ample of tight ors is presented
on local area i aps. In this wo sors is propo g of the underw avigation. The m a single optica ope) is obtaine pect to a well-kn
e-processing st o a known mod using the Iterat 992), while the algorithm devel both sensors a ect, the relative stic camera can g and motion sy ry interesting, elying on a CA n of the system resented metho specific class o ave to mention ruction, propos o apply the epi an optical cam rove the accura o the asymmetr m, the epipola s. The derived c image, the rel a conic section ptical image lies
on systems are sor networks, r
et al., 2013). plications, one sensors is repor finite element w cs with micr il beam sonar acoustic maps lly identifying c ns and then, us mosaic onto the oints.
building 3D m f visual data a
a multibeam s The system formation smoo ultaneously so he camera loca es are treated w rks, while mu
impose the rela m distinct trac itialized with a easured by the r nodes represe s induced by al fashion. In n sensors and t
is enabled th graph itself.
integration be d in (Fusiello an
imaging rather ork, the integra osed in order water scenes an
integration of al camera and a
d by geometric nown model of eps, the acousti del of the object tive Closest Po e optical alignm loped by Lowe are calculated e pose between n be estimated ystem equipme the presented AD model of the m, which may n
odology to a of objects. n a new paradig sed in (Negahda ipolar geometry mera and a 2D s acy of 3D reco rical projection ar geometry, in equations show lative correspon n. Moreover, th s on specific tri
now widely us robotics, video of the first effo rted in (Singh e warps may be us robathymetric
r), obtaining of the seafloor corresponding p sing a finite ele e new mesh de maps of under acquired by a s sonar is present uses pose g othing and map olve for the R
ation in the R within the pose g
ltibeam bathym ative pose const
cklines of the graph that cap onboard navig enting visual fe multibeam su
this solution the extrinsic ca hrough the us etween cameras nd Murino, 200 than the creati ation of optica r to improve nd assist ROV video and 3D a 3D acoustic ca cally registering f the observed s ic data are regis t acquired (an o oint (ICP) algo ment is perform (Lowe, 1991). with respect t n the optical ca
without the ne nt.
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models for an this case, diffe w that for each ndence in the o he matching of gonometric cur
sed in points ement efined rwater single ted in traints dive ptures gation eature ubmap
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3. S
his section we ex t we describe ware specificat ustic camera, i. cal camera, resp
System Conf
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4. EXTRINSIC CALIBRATION OF THE SYSTEM
During the development of the proposed 3D opto-acoustic camera was the alignment of the optical and acoustic 3D data, defined as sensor calibration problem, which allows data from one sensor with the corresponding data of the other sensor. Data alignment, that is, their transformation from each local frame into a common reference frame for both sensors, is of critical importance for the successful deployment of the opto-acoustic fusion system, since the 3D data generated by such sensors are so different from each other that they should be precisely matched in order to detect the spatial coordinates of a given point belonging to an object in both representations. Up to now, the works presented in literature concerning the integration between several types of sonar (single beam sounder, multibeam, 3D acoustic camera) and optical cameras adopt a sensor fusion approach, which is mapping-oriented, according to the classification proposed in (Nicosevici et al., 2008). This means that the data acquired from the two sensors are described through geometric relationships (position and orientation), and the data fusion is performed by means of geometrical correspondences and registration.
The alignment of 3D data is usually solved by performing the extrinsic calibration of the integrated system, i.e. by searching for the fixed - but unknown - rigid transformation which relates the local reference frame of the stereo optical subsystem with that of the acoustic camera, thereby obtaining the relative pose (position and orientation) between the two sensors.
Now, let us assume that the two cameras have already been calibrated independently. In particular, with regard to the stereo optical system, the assumption is that both the intrinsic parameters of each optical camera (focal length, principal point, optical distortions and skew) and those of the extrinsic stereo pair (relative position between the two reference frames) have been determined. Downstream of this calibration process, it is possible to know the 3D coordinates of any point of the scene acquired by the optical subsystem with respect to its local reference frame (typically attached to the left camera of the stereo pair).
Therefore, assuming that a point , , of the optical reference frame corresponds to a point x , y , z of the acoustic reference frame, the rigid transformation that relates the two coordinate systems may be expressed as:
(1) where R = 3 x 3 orthonormal rotation matrix from the acoustic camera to the stereo optical camera reference frame
t = 3D translation vector from the acoustic camera to the stereo optical camera reference frame
Equivalently, indicating with and the homogeneous coordinates of the points and respectively, the aforementioned relation can be expressed as:
p t p (2)
where = 4 x 4 homogeneous transformation matrix from the acoustic camera to the stereo optical camera reference frame
Therefore, our goal is to develop a methodology for the calculation of the extrinsic parameters R and t, which define the
orientation and the position of the acoustic subsystem against the optical one, respectively.
Usually, the calibration methods are highly dependent on the sensors that compose the system and especially on the type of data they provide. In our case, the particular nature of the data acquired by the systems (3D point clouds) makes it possible to implement a methodology for extrinsic calibration based on a "direct" computation of the rigid transformation matrix that relates the reference systems associated with the optical and acoustic sensors. This is achieved through a simple registration of each pair of optical and acoustic 3D point clouds of a planar pattern, which is used as a target in the calibration procedure and acquired in different poses. Finally, the ability to obtain multiple estimates of the transformation matrix allows for implementing an appropriate optimization technique, in order to obtain more accurate results.
The implemented methodology is composed of two separate data-processing threads that are related to the two sensory channels, which eventually merge in the last stages of the proposed solution.
Starting from the synchronous acquisition of the n poses of the calibration panel during the early stage of the process, the entire methodology is aimed to obtain n pairs of 3D point clouds, where the n-th pair is formed by the optical , and the acoustic , 3D point clouds, in order to calculate, by means of coarse and fine registration algorithms, n estimates , of the rigid transformation matrix. At the end of the process, the final transformation matrix ∗ is obtained by processing the dataset composed of n transformation matrices , obtained downstream of the previous registration stage.
4.1 Acoustic image processing
The 3D image provided by the acoustic camera can be corrupted either by false reflections caused by the secondary lobes of the receiving array or by the noise present in the acquisition phase of the backscattering signals. The latter is modelled as speckle noise. The secondary lobes are responsible for the blurring of the object, while the speckle noise causes a low response or no response at all within the object itself.
So it is evident that the operations of filtering (noise reduction and the elimination of possible outliers) and segmentation (differentiation of objects and background in the observed scene) are to be considered as preliminary and mandatory steps for the execution of all fusion algorithms to be applied to this specific type of data (Murino et al., 2000a).
While in literature there are a number of algorithms for filtering and segmentation with variable results and degrees of automation (Murino, 2001b)
,
the solution adopted in this calibration method for the processing of the acoustic 3D point clouds representing the calibration panel in its different poses, provides for a completely manual filtering and segmentation procedure, performed through the open source software CloudCompare (CloudCompare, 2014), as the implementation of an automated procedure would require further, more focused research.4.2 Optical stereo images processing
This subsection describes the procedures for the creation of a 3D point cloud representing the optical calibration panel in its different poses, starting from the acquisition of a pair of stereo images.
4.2.1 Image enhancement Underwater images are
limited visibili channel) and n quality of the reconstruction Regarding th solution make based on the al., 1992) an procedure (HI
4.2.2 Ster
needed to co (focal length, tangential dist (translation an system) of the are computed Toolbox for M
4.2.3 Che
the calibration through well-order to obtain each pose assu The 3D reco (OpenCV 201 particular, the the stereo pair of the stereo disparity map the product of the calibration
4.1 Coarse
This subsect methodology t the unknown r Considering th point clouds, composition o fine registratio Taking as a r stereo optical out through tw
1. Calculatio the acoust that the Z the optical
2. Alignment an automa from an es clouds, de (assuming As a resul matrix For what c implementatio Closest Point
, , align
transformation Downstream transformation
ity range, color noise. All these e results obtain n technique (Ga he proposed c
es use of a me technique of H nd on a subs
ST).
reo optical cali
mpute the intr coordinates o tortions, pixel nd rotation wi e camera and th d using the Matlab (Bouguet
ckerboard 3D
n process, the known techniq n the 3D point umed during the onstruction was 14) and Libelas OpenCV libra r, while Libela matching algo . Finally, the 3 f the reprojecti n process and th
and fine regist
tion describes that allows for rigid transforma he n-th pair , the associat of transformati on algorithms.
eference system camera, the co wo operations: on of , by
tic camera loca axis represents l system; t of the pair of atic algorithm im stimate of the ce etermines the tr g that the rotatio lt of this opera
, .
concerns the on in CloudCom
algorithm (IC ned in the pre n matrix , . of the previou n matrix ,
, ,
r attenuation (p e issues have a ned in the app
llo, 2013). calibration me ethodology of Histogram Stre sequent manua
bration A cali
rinsic paramete of the principa
size) and the ith respect to he stereo system
well-known C t, 2013).
D reconstructio
acquired imag ques of stereo
clouds of the e acquisition sta s carried out s (Geiger et al. ary was used fo as was used for orithm and the 3D point cloud
on matrix obta he disparity map
tration
the steps obtaining the n ation matrix.
, , of acou
ted , is ions obtained m the local ref oarse registratio
means of a m al reference fra s the depth of th
3D point cloud mplemented in enter of gravity ranslation vecto on matrix R is u ation, we obtain
step of fin mpare of the w CP) has been evious step, in
us operations, is obtained as (
,
articularly in th strong impact o plication of an ethod, the ad image enhance tching (Gonzal al color retou
ibration proced ers of each ca l points, radia extrinsic param
a world coord m. These param Camera Calibr
on Downstrea
ges were proc photogrammet calibration pan age.
using the Ope ., 2011) librarie or the rectificati
r the implement e generation o d was determin ained downstrea
p itself.
of the calibr n estimates
ustic and optica determined through coarse ference frame o on stage was ca
manual orientati ame, in such a he scene, in line
ds , , thr CloudCompare y of the two 3D or t that relates unitary).
n the transform
ne registration well-known Iter
applied to the order to obtai the unknown (figure 4):
,
he red on the ny 3D dopted
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algorithm opera for each , , acoustic point corresponding 3 for each pair points of the op of the acoustic 3 calculates a selects , a assumes as
, .
5. CAM
gned with the a niques current osed system is nstruction of th cerning the aco lution image by Detection mo isotropic scan coarse positio Classification acoustic came fine scan of i is automatical the system de manually, by
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ates in the follo it applies this t clouds, in or 3D optical point
, , , the me
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∗ the transform
MERA CONTR
aim of integratin tly available
able to provide e acquired scen oustic subsystem y working in tw
de: in this mod n of the scene on of a target; n mode: after
era switches to ts FOV (60˚ x lly activated wh etects a target setting the focu
d, the stereo o uired scene thr
tions for compu x.
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ng has been im ormation matrix
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culated;
, ;
m value of ; mation , co
ROL SOFTWA
ng the most rec in scientific de an on-line op
ne.
m, it provides wo different mod dality the syste
e in order to
the target iden this modality 40˚). The class hen, in the prev at a range les us distance.
optical subsyste rough a digital
uting the ,
on matrices
mplemented in x ∗ from the ration of coarse of 3D point implemented in es the final rom the ,
to the , 3D them with the
, between the
sponding points
orresponding to
ARE
cent data fusion literature, the pto-acoustic 3D a high angular dalities: em performs an
determine the
ntification, the and performs a sification mode vious modality, ss than 2 m or
stereo pair, an using different A custom softw of the image d easily interpre It was concei following requ 1. a customiz A first prototy The system is modes. In this one screen, or information is the ROV opera For example, depth of the sc
Figur
Figure 6.
In this work development provide an underwater sc optical camera installed on a by the operato or in close ran
nd also to prov t stereo matchin ware is being d devices and for etable representa
ived and desig uirements:
zable interface on according to s;
xibility such tha main paramete cope with the er environment. ype of the user i s able to switc s way, 2D and 3 r in suitable dis s enhanced thro ator during spe
Figure 6 show cene is coded th
re 5. Prototype o wi
. Display windo RG
6. CON
, we have pr of an opto-ac on-line 3D r cene.The syste a and 3D acou ROV in order or during the na nge manipulatio
vide an on-lin ng algorithms. developed for su
r providing RO ation of the visu gned with the
to manage and different user p
at the operator ers of the opto variable cond
interface is show ch between di 3D information
splay windows ough additional cific work activ ws a display w
hrough a RGB s
of the user inter indow layout.
ow with depth m GB scalar field.
NCLUSION
resented a firs coustic camera reconstruction
m is composed ustic camera, an to improve its avigation in turb on operations. I
e 3D reconstru upporting the co OV operators wi ual information aim of meetin
d organize the v profiles and wo
is able to set, in o-acoustic came itions typical o
w in figure 5. fferent visualiz
can be display in which the v l data that may vities. window in whic
scalar field.
rface and contro
map coded throu
st step toward a, which is ab of the exp d of a custom s nd is designed s control capab bid water cond n order to perfo
uction ontrol ith an n. ng the
visual orking
n real era in of an
zation yed on visual y help plored
stereo to be bilities ditions form a
ect sensor fusi ed to determine reference system
osed. The cali ern as a target, w
ont of the opto ds provided by wo-step proces rithms and the stical processin developed a pro ustic camera an visual informati software allow verify its usab ly interpretable
7. AC
work has been PON01_02140 C’ 2007/2013 (
, P. J., & McKa hapes. In: Robot ety for Optics a nco, G., Gallo,
parative analys erwater 3D rec itzerland), Vol. guet. Webpa e 2014). no, F., Bianco
onale, A. V., 2 eo vision for un
hotogrammetry udCompare. W 4).
ello, A., and M visualization gration. Visualiz nsactions on, 10
o, A., 2013. In nstruction of Cu ger, A., Roser, e stereo match 8. Springer Ber zalez R.C., a essing. Addison leghi, B., Kham 3. Multisensor Information Fus
z, C., and Sing visual infor cles. Journal of
ion, an extrins e the rigid trans ms associated w ibration was c which has been o-acoustic syste the two camer ss based on c en the final ma
g.
ototype of the so d is able to pro on supplied by wed us to perfo bility. It also pr
representation
CKNOWLED
n partially suppo 0), financed by (D.D. Prot. n. 0
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