Real time map generation using sidescan sonar scanlines for unmanned underwater vehicles

Ocean Engineering 91 (2014) 252–262

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Ocean Engineering journal homepage: www.elsevier.com/locate/oceaneng

Real time map generation using sidescan sonar scanlines for unmanned underwater vehicles Edward Chen, Jenhwa Guo n National Taiwan University, Department of Engineering Science and Ocean Engineering, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan, ROC

art ic l e i nf o

a b s t r a c t

Article history: Received 9 October 2013 Accepted 10 September 2014

This work presents a method for mapping the seafloor in real time using scanline information of sidescan sonar on an unmanned underwater vehicle (UUV). The mapping system combines information about the motion of the UUV with observations made using sidescan sonar. The observation process detects environmental features and locates them relative to the UUV. The procedure localizes the feature and calculates the sidescan sonar footprint. An occupancy grid mapping algorithm is then used to map the seafloor, combining uncertainties in the motion of the UUV with measurement errors from the observations. This procedure was implemented on a UUV to verify its landmark detection and mapping capability. The experiment conducted on a sandy seabed with concrete targets is presented. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Unmanned underwater vehicles Sidescan sonar Navigation Mapping

1. Introduction Unmanned underwater vehicles (UUVs) are often equipped with sidescan sonar which produces images of the seabed (Fish, 1990; Mazel, 1985). Sonar signals that are received following backscattering and reflection from the seafloor are generally enhanced by time varying amplification. The fundamental process of scanline formation is to propagate acoustic signal through the sea water, and subsequently the energy is scattered from the seafloor (the so called backscattered signal) and received by the transducers. The returned energy to the transducer is then used to create one scanline, and displayed as a line of pixels with the grayscale value of each pixel determined by the intensity returned at the instant. Bell and Linnett (1997) utilized Rayleigh distribution to verify the results of their sidescan sonar simulation. The imaging process can be simulated using bistatic models to construct sidescan sonar simulators. Sidescan sonar transmits signals in both vertical and horizontal planes, covering an area that is called the “footprint”. The resolutions in the direction of the sonar track and across the direction differ (Lurton, 2002; Medwin, 1975; Chen and Millero, 1977). Rayleigh distribution and K-distribution are general statistical models of scattering signals that can be utilized to classify a seafloor. Rayleigh distribution uses the assumption of a diffuse scattered field and it neglects the reflection in the specular direction. For strong scattering (for example, in this work we have the hard seabed condition), K-distribution may provide a better representation for the scattered signals.

n

Corresponding author. E-mail address: [email protected] (J. Guo).

http://dx.doi.org/10.1016/j.oceaneng.2014.09.017 0029-8018/& 2014 Elsevier Ltd. All rights reserved.

The K-distribution has been found to provide a good representation in some signals, such as radar images of the sea (Jiang et al., 1998). Dunlop (1997) pointed out the relationship among probabilistic distribution models of some terrains which could be used to classify the seafloor, but poor agreement was found in sand and finer sediment seabed condition from sidescan sonar data by using only K-distribution. K-distribution has a modified Bessel function term in its mathematical representation which creates the computing burden in finding out the exact solution in real time. The conventional classification with sidescan images modeled by K-distribution is operated in offline. Although Gordon and Ritcey (1995) proposed a fast computing method to replace a scattering model in K-distribution to reduce computing time, but it is still very limited to implement it in real time for UUV navigation. To resolve the real-time issue of the statistical models applied to the seafloor characterization using sidescan sonar, a general model with a simple computation process for the analysis of the backscattering is necessary. Recently the Nakagami distribution, initially developed to describe the statistics of echo envelope in radar and wireless communication systems (Patzold, 2002) was proposed to fit the statistics of envelope of ultrasonic backscattered echoes from human tissues (Tsui et al., 2006). The literature reference indicates that ultrasonic backscattered echoes from human tissues can be modeled by the K-distribution as well as the Nakagami distribution. Parameters in the Nakagami distribution can be calculated much easier compared to the parameters of the K-distribution. In our experiments, the Nakagami distribution was used to model the scattered signals of sand and hard seabeds for the construction of a seabed detector. The other component of our seabed detector is an adaptive threshold algorithm employing the constant false-alarm rate (CFAR) technique. The CFAR is adopted

E. Chen, J. Guo / Ocean Engineering 91 (2014) 252–262

to detect a target signal among radar signals (Rohling, 1983; Gandhi and Kassam, 1988) or in a communication system (Kim et al., 1999). A successful application of CFAR to detect targets automatically by radar has been described in (Jiang et al., 1998). A combination of CFAR combined with Target Presence Probability was proposed to detect landmarks for the localization of autonomous vehicles and mapping (Mullane et al., 2007). Information extracted from sidescan sonar data could support the navigation of UUVs in an unstructured environment. Sidescan sonar images of the seafloor typically consist of a series of scanlines, one per transmission-reception cycle, displayed perpendicularly to the survey track. A number of scanlines are necessary to make an image. To navigate an UUV around a landmark, the position of the landmark should be detected with high confidence. The range and resolution of sidescan sonar is physically limited, and noisy navigation sensors affect the accuracy of motion of the UUV (Cobra et al., 1992; Anstee, 1994). Detection of a target using a sidescan image is normally carried out offline. An investigation of underwater vehicle localization using sidescan sonar images have been published in (Tena Ruiz et al., 2004). This work demonstrated the superiority of a smoothing filter when applied to autonomous underwater vehicles (AUVs) mapping using sidescan images. Relevant to this research, progress in the AUV navigation of using range measurements with the aid of sidescan images can be found in Zerr et al. (2005). There are researches that focus on the seabed classification and image segmentation techniques of sidescan images that can be potentially applied in AUV navigation (Carmichael et al., 1996; Nait-Chabane et al., 2013; Fallon et al., 2011). The approaches for deep-sea AUV navigation using sidescan sonar images to perform underwater mapping and localization were introduced in Aulinas et al. (2010). Woock proposed ideas of a simultaneously mapping and localization framework by fusing sidescan data with inertial sensors outputs (Woock and Frey May, 2010; Woock, 2011). Line relief maps of sidescan sonar will be utilized to extract shape features as navigation landmarks of AUV. In the reference literatures listed above, sidescan information are all in the form of 2D or 3D images which are the results of stacked scanlines. Real time localization using sidescan images is still being examined since the landmark images must be processed after data on a certain area of sea floor have been collected, generally resulting in time delays in the use of data for navigation. Accordingly, instead of the use of sidescan sonar for mapping the environment off-line, a procedure for mapping the seafloor and navigation using sidescan sonar scanline in real-time is developed herein. The need to process in real time information to support navigation imposes a strict computational requirement. Sampling coordinates of a scanline can be estimated by the UUVs onboard sensors of roll, pitch, and yaw angles. Furthermore, layover due to vehicle rolling can be detected by roll sensors and inappropriate data with large rolling angles will be discarded. The target location on the seafloor detected by our algorithm is represented as occupancy grid which is an algorithm for approximately positioning of navigational landmarks represented by a grid map, achieving timely responses by sacrificing accuracy (Thrun et al., 2005). The grid-based map differs from methods for representing landmarks as a feature-based map using data that have been collected by sensors calculates the spatial relationships between the “features” and the uncertainty of motion (Smith et al., 1986). The calculation time based on features could be long in cases when there are many features in the map. The occupancy grid is a representation of an environment using an array of cells, each of which is either represented as an occupied or empty grid (Moravec and Elfes, 1985; Elfes, 1989). Many navigation problems by using grid map can be found in literatures in robotics, for example, Matthies and Elfes (1988) solved the mapping problem using a probabilistic framework, such as a Bayesian framework. Thrun (1993) obtained grid maps using a neural network-based approach, and developed the inverse measurement model using

253

a binary Bayes filter and a forward model approach (Thrun, 2003). In this work, the occupancy grid cells were used to describe the detected seafloor to limit the number of target points relative to the feature map method, therefore, to reduce the computational time for implementation of real time UUVs' navigation. This work examines a grid-based mapping method for locating the positions of targets on the sea floor in real time for the purposes of mapping and navigation of UUVs. Information from scanlines instead of sidescan images are used to detect the positions of landmarks. This article is composed of five sections. Section 2 introduces the observation using sidescan sonar and the problem of mapping using scanline information. Section 3 elucidates the observation technique. Section 4 presents experimental results. Finally, Section 5 draws conclusions.

2. Map modeling This section proposes the construction of a grid map from scanlines. The map can be updated as new observations are being made. The footprint of observation model is the region that is covered by the sidescan sonar. The measurement errors in pitch, roll, and yaw of the UUV affect the uncertainty of the location of the feature being observed. Modeling the environment using grid maps has several advantages. Grid maps offer more flexible representation of targets than feature-based maps. The resolution of the map can be adjusted for the quality of the task. For noisy sensors, it is hard to extract features from the measurements in real time due to large uncertainties in the target position. Therefore, instead of representing the targets using feature-based map, grid-based approaches are more appropriate. The occupancy grid mapping method creates a twodimensional array of cell which corresponds to a horizontal grid of the desired resolution. Each cell, L, has association with a variable, o(L), which is a discrete random variable with two states, occupied and empty. The probabilistic estimate of state of each cell is defined as P(o(L)¼ occ) and P(o(L)¼emp), the probability of occupied cell and empty cell, respectively. These states are mutually exclusive and exhaustive such that the probability of cell i in the map can be represented as PðoðLi Þ ¼ occÞ ¼ 1  PðoðLi Þ ¼ empÞ. Several measurements may be associated with each grid cell. Given the sensor reading z1:t from the sidescan sonar and the state x1:t of the vehicle, the desired probability of the grid cell can be defined as a conditional probability P(o(Li)¼occ|z1:t,x1:t) and the estimation of the probability of occupancy in each grid cell is a binary estimation problem. A binary Bayes filter was used to solve this problem. The advantage of the binary Bayes filter is that the filter uses the log odds over the probability representation to avoid numerical instabilities for probabilities near zero or one. The odds of the variable, o(L), is defined as the ratio of the probability, P(o(Li)), of this event divided by probability of its negative event (Thrun et al., 2005). PðoðLi ÞÞ PðoðLi ÞÞ ¼ Pð  oðLi ÞÞ 1  PðoðLi ÞÞ

ð1Þ

The log odds, l(o(Li)), is the logarithm of this expression. lðoðLi ÞÞ ¼ log

PðoðLi ÞÞ 1 PðoðLi ÞÞ

ð2Þ

The mapping problem can be represented as lt;i ¼ log

PðoðLi Þ ¼ occjz1:t ; x1:t Þ 1 PðoðLi Þ ¼ occjz1:t ; x1:t Þ

ð3Þ

Then, the probabilities can be recovered from the log odds ratio, PðoðLi Þ ¼ occjz1:t ; x1:t Þ ¼ 1 

1
 1 þ exp lt;i

ð4Þ

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Table 1 Occupancy grid mapping algorithm. 1 2

/Algorithm occupancy grid mapping/ for all grid cell Li do

3

l0;i ¼ log

PðoðLi Þ ¼ occÞ 1  PðoðLi Þ ¼ occÞ

4 5 6 7 8 9 10 11 12

end for /Grid calculation using binary Bayes filter/ for every time step t for all cell Li in perceptual filed of zt then do lt;i ¼ lt  1;i þ IðLi ; xt ; zt Þ  l0 end for end for /Recovery of occupancy probability/ for all grid cells Li do   1 13 PðoðLi Þ ¼ occz1:t ; x1:t Þ ¼ 1  1 þ exp flt;i g 14 end for

Fig. 2. Back-scanning due to pitching of the vehicle, Φp is the pitch angle of UUV.

Fig. 1. Rolling effect, Φr is the roll angle of UUV.

Fig. 3. Relative positions between the sidescan sonar scanning footprints with and without vehicle pitching, Φy is the yaw angle of UUV.

Table 1 summaries the occupancy grid mapping algorithm of ðPðoðLi Þ ¼ occÞ; xt ; zt Þ. Line 8 of Table 1 is a recursive equation with an initialization constant, l0 as the prior of occupancy. The function I here is used to implement the inverse measurement model PðoðLi Þ ¼ occjzt ; xt Þ in its log odds form. IðLi ¼ occ; xt ; zt Þ ¼ log

PðoðLi Þ ¼ occjzt ; xt Þ 1 PðoðLi Þ ¼ occjzt ; xt Þ

ð5Þ

which specifies the odds of occupancy of the grid cell o(Li) conditioned on the measurement zt and vehicle's pose xt. This log odds constitutes an inverse sensor model, since it maps sensor measurements back to their causes. The probability associated with a grid cell can be obtained from the sidescan sonar observation, and the corresponding navigation data. Owing to the effects of motion on the sensor footprint, the position of the target should be corrected according to the vehicle's pitch, roll, and yaw angle before the target can be located. Cobra elucidated the effect of these three angles (Cobra et al., 1992). Fig. 1 displays the effect of rolling motion on the sonar coverage. The area within the dashed line is the coverage area at zero rolling angle and the areas in the solid line is that covered by the sonar when the vehicle rolls. Pitch yields the forward and the back scans that are shown in Fig. 2. The dashed line in that figure corresponds to horizontal motion of the vehicle and the solid line captures the backward scan at an angle of Φp. Fig. 3 presents the effect of pitch on the position of the sonar footprint. After correction for the motion and slant of the vehicle, every detected target has a coordinate that is related to the center of the UUV. The position of the target can be represented by (Anstee, 1994), 2 03 2 3 x x 6 07 7 n6 ð6Þ 4 y 5¼T 4 y 5 0 H H

where (x, y) is the position relative to the center of the UUV; x is the across-track direction; y is the along-track direction; (x0 , y0 ) is the corrected position, and H represents the height of the vehicle above the seafloor and is given by H ¼ T b ðC=2Þ

ð7Þ

Tb is the time of the first return of the sonar beam, and C denotes the speed of sound in water. A general transformation T that involves all three rotations requires an ordering convention to make it unique. To preserve the uniqueness of the coordinate system, yaw, then pitch, then roll transformations are performed to yield the general transformation. 2 3 cΦy cΦr c Φr s Φy sΦr 6 sΦp sΦy sΦr  cΦp cΦy  sΦp cΦr 7 T ¼ 4 sΦp sΦr cΦy  cΦp sΦy 5 cΦp sΦr cΦy  sΦp sΦy  cΦp sΦy sΦr þ sΦp cΦy c Φp c Φr ð8Þ where c is cosine and s is sine. Φy is the heading angle of the vehicle, Φp is the pitch angle, (positive indicates raising of the bow), and Φr is the roll angle (positive indicates raising of the port side). The position of the center of the UUV in global coordinates is as follows. X g1 ¼ ½xg1 yg1 H g1 Φpg1 Φyg1 Φrg1 T

ð9Þ

where xg1 , yg1 and H g1 locate the UUV in global coordinates. Subscripts g indicates the global coordinate, and subscript 1 represents the coordinate of the UUV originated at the center of the vehicle. Φpg1 , Φyg1 , and Φrg1 are the included angles between the UUV coordinate and the three axes in global coordinates. Assume that the error of the UUV location has a Gaussian distribution and sensor noises are independent, the covariance matrix can then be

E. Chen, J. Guo / Ocean Engineering 91 (2014) 252–262

shown as follows:   Cg1 ¼ diag σ 2xg1 ; σ 2yg1 ; σ 2Hg1 ; σ 2Φpg1 ; σ 2Φyg1 ; σ 2Φrg1

ð10Þ

where the diagonal terms are standard deviations relative to the components in X 1 . The position of the target relative to the center of the UUV is as follows: X 12 ¼ ½x12 y12 H 12 Φp12 Φy12 Φr12 T

ð11Þ

where subscript 2 defines the coordinate of the target and x12 is the horizontal distance from the target to the UUV center, and is as follows: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x12 ¼ R212 H 212 ð12Þ R12 is the slant range, the straight-path time of arrival of a sonar signal along the hypotenuse of a triangle described by the sidescan sonar, the seafloor directly below it, and the seabed target, and H 12 is the height of the UUV above the seafloor. Moreover, the termy12 is the distance along the track to the center of the UUV, which is zero by the definition of the coordinate. The three angles of the target relative to the center of UUV are obtained as follows: 3 2 3 2 3 2 0 atan 2ðH 12 ; y12 Þ Φp12 7 6 Φ 7 6 atan 2ðy ; x Þ 7 6 0 ð13Þ 5 4 y12 5 ¼ 4 12 12 5 ¼ 4 ; x Þ atan 2ðH ; x Þ atan 2ðH Φr12 12 12 12 12 where Φp12 is the included angle between the x axis and the UUV, Φy12 and Φr12 are the included angles between the z axis and y axis, respectively, and the UUV. The across-track resolution and the along-track resolution vary with the slant range. Hence, assume that the resolution is the standard deviation of the distance from the target to the UUV in the x and y directions. The covariance matrix is as follows:   C12 ¼ diag σ 2x12 ; σ 2y12 ; σ 2H12 ; σ 2Φp12 ; σ 2Φy12 ; σ 2Φr12 ð14Þ The diagonal terms are as follows. 3 2 2 3 σ 2x 1 CT d 6 12 7 6 σ 2 7 6 2 2 sin ðθÞ 7 6 y12 7 6  7 6 2 7 6 1 Rφ 2 7 6σ 7 6 7 6 H12 7 6 2 7 6 2 7¼6 7 0 6σ 7 6 7 6 Φp12 7 6 7 0 6 2 7 6 7 6σ 7 6 7 6 Φy12 7 4 5 0 4 5 σ 2Φr12 0

255

specify the location of the target in global coordinates, as follows: 2 3 2 3 2 3 2 0 3 2 3 xg1 xg1 xg2 x12 x12 6y 7 6 7 6 7 6 0 7 6 7 ð17Þ 4 g2 5 ¼ Tg1 4 y12 5 þ 4 yg2 5 ¼ 4 y12 5 þ 4 yg2 5 H g2 H g1 H g1 H 012 H 12 Then, Φpg2 , Φyg2 , and Φrg2 are the included angles in global coordinates. 3 2 3 2 Φpg2 atan2ðH 012 ; y012 Þ 6 Φ 7 6 atan2ðy0 ; x0 Þ 7 ð18Þ 4 yg2 5 ¼ 4 12 12 5 Φrg2 atan2ðH 012 ; x012 Þ The term Tg1 is the rotation matrix, which is obtained from Eq. (8). The covariance of the location vector of the target in global coordinates is estimated using the compounding Jacobian, Cg2 ¼ J1  Cg1 JT1  þ J2  C12 JT2 

ð19Þ

The term Cg2 is the first-order estimate of the covariance matrix which represents the distribution of positions of targets in global coordinates. J1  and J2  denote the left and right half matrices of the compounding Jacobian.

 ð20Þ J  ¼ J1  J2  J ¼

∂ðX g1  X 12 Þ ∂ðX g2 Þ ¼ ∂ðX g1 ; X 12 Þ ∂ðX g1 ; X 12 Þ

ð21Þ

The procedure of calculate the inverse measurement is shown in Table 2. Line 2 to line 3 is the procedure of calculating the estimate of first order covariance matrix. Figs. 4 and 5 display the results of applying the inverse measurement model. The covariance, represented by an ellipse was obtained using Eq. (19) by choosing one standard deviation. Once the detection result is with target presence, the algorithm will return locc to represent the log odds of the cell. Also, the log odds lemp will be obtained if detection result indicates target absence. Otherwise, if the cell is outside of the region of footprint, this model will assign to this cell with a

2

Table 2 Inverse measurement model algorithm.

ð15Þ

where σ x12 is the across-track resolution, and σ y12 is the alongtrack resolution (Mazel 1985), θ is the incident angle, and φ is the aperture of a sidescan sonar beam in the horizontal plane. R denotes the slant range of a target, and T d is the transmission time (pulse duration) of the transmitted signal. To combine the uncertainties associated with the motion sensors and the uncertainties in the sidescan sonar measurements, the position of the target in global coordinate is established as, 2 3 xg2 6 y 7 6 g2 7 6 7 6 H g2 7 6 7 X g2 ¼ 6 ð16Þ 7 ¼ X g1  X 12 6 Φpg2 7 6 7 6 Φyg2 7 4 5

1 2

/Algorithm inverse measurement model/ X g2 ¼ X g1  X 12

3 4 5 6 7 8 9 10 11 12

Cg2  J1  Cg1 JT1  þ J2  C12 JT2  if the center-of-mass of Li is in the area of the 2D ellipse if target present return locc else return lemp end if else return l0 end if

z

R x

y

Φrg2

The symbol  indicates the compounding of vectors (Smith et al., 1986). Compounding is an operation that calculates estimates of the mean and covariance of the position and orientation of a frame relative to the other frame. The terms, xg2 ,yg2 , and H g2

Fig. 4. The geometry of measurement uncertainty for long slant range. The acrosstrack resolution is smaller than the along-track resolution.

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z

Feature Extraction

R

x

Energy

y

Distribution Parameter Distribution Error

Fig. 5. The geometry of measurement uncertainty for short slant range. Unlike the case shown in Fig. 4, the across-track resolution is larger than the along-track resolution.

prior value l0 . The probability of P(o(Li)¼occ) is generally determined based on the confidence of the detection algorithm. locc , lemp are then calculated by Eq. (5). The constant l0 is the prior of occupancy, and the term P(o(Li) ¼occ) of l0 is usually set to 0.5, depending on the expected target density on the seafloor.

3. Detection method The purpose of target detector is to determine the probability of target presented in a scanline. The seabed is assumed to be horizontal and planar, a commonly made assumptions for sidescan users. When the seabed is not flat, geometrical correction requires a priori assumptions about the seafloor topography. Otherwise, additional bathymetric data are needed by the sidescan sonar equipped with an interferometer. The sonar scanlines are firstly normalized using a moving average algorithm to minimize the variations of signal from different seafloor and slant ranges. I n ðtÞ ¼

1 2W þ 1

I r ðtÞ W

W

ð22Þ

∑ I r ðt  nÞ þ ∑ I r ðt þ nÞ þI r ðtÞ

n¼1

Naïve Bayesian Detector

Fig. 6. Detector signal processing diagram.

Values of M from 0 to 1 reflect density functions ranging from pre-Rayleigh to Rayleigh distribution, and values above 1 correspond to the distribution of post-Rayleigh distributions. Scatters from different seabed can also be differentiated by the Nakagami parameter. The distribution error, Error, which is the difference between a scanline signal histogram and its Nakagami distribution fit, is defined as !2 255

Error ¼

∑ ðN ðg Þ  Gðg ÞÞ

ð25Þ

g¼0

where N is the Nakagami probability distribution function, g is the gray level, and G stands for signal histogram. A Naïve Bayesian detector is a simple probabilistic detector that is based on Bayes' theorem and the naïve assumption. The naïve assumption is that all attributes are all mutually independent. Any new datum instance Fi is detected as hNaive

Bayes

¼ arg max h A ½yes;no

PðhÞPðF i jhÞ PðF i Þ

ð26Þ

n¼1

where W is window size, t is time, Ir is the signal and In is normalized signal. The detection algorithm extracts targets from the sonar data according to parameters of their pre-selected features. Log-energy, distribution parameters and the distribution error between the estimated distribution and the histogram are the features that are mostly commonly exploited by detectors of the seafloor (Jiang et al., 1998; Atal and Rabiner, 1976). Fig. 6 displays the data processing steps of the detector. After features are extracted from the scanline data, a Bayesian detector is employed to detect the target. The log-energy value, En, which is computed from the signal energy in a log form can be represented as,

1 b En ¼ log ε þ ∑ I F 2 ðnÞ ð23Þ bn¼0 where ε is a small positive constant added to prevent the computing of log of zero, b is signal length, and IF donated scanline signals in the frequency domain. Nakagami distribution model in radar and wireless communication systems was derived from the fundamental physics of scattering based on the assumptions made by Nakagami (1960). The distribution shape parameter, M, which is the shape parameter of the Nakagami density function, indicates the statistical distribution of the signal, h  i2 E E In 2 M¼ h ð24Þ  i 2 E In 2  E In 2

where P is the probability function and h is a maximum likelihood hypothesis. Since the evidence is a constant if the values of the feature variables are known, Eq. (26) can be further rewritten as hNaive

Bayes

¼ arg maxPðhÞ∏ PðF i jhÞ h A ½yes;no

ð27Þ

i

Eq. (27) can be rewritten as Eq. (28) for the three features presented in Eqs. (23–25).   ð28Þ h ¼ arg max fP T p nP F 1 ; F 2 ; F 3 jT p ; P ðT ab ÞnP ðF 1 ; F 2 ; F 3 jT ab Þg where P (Tp) denotes the probability that a target is present in grid cells and P (Tab) is the probability that the target is absent. F1 is the log-energy value; F2 is the distribution shape parameter, and F3 is the distribution error.

4. Experiments Fig. 7 presents an image of the target object which is the concrete water intake nozzles of a power plant on the seafloor. The target image was generated by a multi-beam sonar being used here as a ground truth for sidescan map verification. The environmental background was a sandy seabed and the dimensions of the target were approximately 100 m by 150 m. Fig. 8 shows the photo and the layout of the UUV equipped with two sidescan sonar transducers, one on the port side and the other one on the starboard side. Table 3 compares detection errors obtained using a combination of two features with that obtained using three features. In this test, the total sidescan sonar scanlines was 600, in which 250 of them were with target and 350 scanlines were

E. Chen, J. Guo / Ocean Engineering 91 (2014) 252–262

257

without target. In Table 3, Target Presence Error represents the quantity of detection error in the 250 scanlines with targets and Target Absence Error represents the quantity of detection error in the 350 scanlines without any targets. From the table, applying three features for detection has better performance in smaller error rate (5.5%). It indicates that the detector performs well when three features are adopted and that its performance is degraded when any combinations of two features are utilized. Thresholding is applied to segment signals. Constant thresholding for detecting target features may fail because of noise. Adaptive thresholding has the advantage that the threshold can be adjusted according to the noise level in the signal. The Constant False Alarm Rate (CFAR) has been extensively used in the field of Radar signal processing. This study uses an adaptive threshold algorithm that is based on

Fig. 7. Target overview.

Fig. 8. The picture of the UUV (above) and the layout of the UUV.

Table 3 Detection performance Features

F1 F2 F1 F1

& & & &

F2 F3 F3 F2 & F3

Target present (250 scanlines)

Target absent (350 scanlines)

Total error (600 scanlines)

Mis-detection

Error rate (%)

False alarm

Error rate (%)

No. of error

Error rate (%)

20 26 15 9

8 10.4 6 3.6

24 19 19 24

6.9 5.4 5.4 6.9

44 45 34 33

7.3 7.5 5.7 5.5

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Fig. 9. Thresholding performance with no target in signal.

Fig. 11. Functional block of the mapping process.

Table 4 Sidescan sonar specifications.

Fig. 10. Thresholding performance with target in signal.

CFAR to detect the target in a complex background. Figs. 9 and 10 display scanline signals with and without target information, respectively. The target was detected by adaptive thresholding. During the measurement, at each time step t, the local map Lt could be obtained by given the UUV pose X t and the scanline measurement Z t , the procedure in a single time step is shown in Fig. 11. The target detection block detects the presence without deciding the location of the target, and the location of the target were obtained in the occupancy grid mapping block. Finally, the system use local map to update the global map in every time step. Table 4 presents the sidescan sonar parameters. The vehicle was piloted along pre-planned paths over the area above the target when the sidescan data are being collected. Figs. 12 and 14 display maps that were obtained without taking motion-related uncertainties into account. Fig. 12 was obtained by mapping without motion correction using a grid resolution of 1 m. The grid map is distorted in the occupied area. Figs. 13 and 15 display maps with correction for the motion of UUV using the same grid resolution. Clearly, the correction reduced the distortions. The occupied areas far from the UUV were associated with

Specification

Description

Operation frequency Scan rate Sampling interval Pixels in each scanline Slant range Pulse interval Beam width

400 KHz 5 Hz 15360 ns 12574 145 m 0.1 ms Horizontal 0.41; vertical 501 Depression 201 downward Yaw accuracy o 1.51 RMS Roll, pitch accuracy: 70.41

Yaw/pitch/roll

larger errors than the near occupied areas. Fig. 16 shows the true size of the targets that were identified using multi-beam sonar, and Fig. 17 presents the occupancy map that is constructed from the true map. The accuracy of mapping is evaluated by calculating a correlation coefficient between the true map and the resultant map constructed by the sidescan sonar. Correlation coefficients were obtained using Eq. 29, with the GPS position on the back of the UUV adopted to align the occupancy map obtained from the true map with maps generated by the sidescan sonar.

ρm1 ;m2 ¼

Covðm1 ; m2 Þ Eðm1 m2 Þ  Eðm1 ÞEðm2 Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   σ ðm1 Þσ ðm2 Þ E m 2  E2 ðm Þ E m 2 E2 ðm Þ 1

1

2

ð29Þ

2

where m1 and m2 denote the grid map from the true map and the map that is generated from the sidescan sonar. Table 5 lists the

E. Chen, J. Guo / Ocean Engineering 91 (2014) 252–262

resolution 1m without motion correction

0

0

50

50

100

100

meters

meter

resolution 1m

150

150

200

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0

50

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259

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450

0

50

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meter

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meters

Fig. 12. Grid map constructed by treating scanlines with no uncertainties. No compensation on the effects of the pitch, roll and yaw.

Fig. 14. Grid map constructed by treating scanlines with uncertainties. Effects of the pitch, roll and yaw were not considered to correct the map. Map distortions are less than that shown in Fig. 12. More grid areas were covered by scanlines.

resolution 1m resolution 1m with motion correction 0 0 50 50

100

meters

meters

100

150

150

200 200 250 250 0

50

100

150

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250

300

350

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meters Fig. 13. Grid map constructed by treating scanlines with no uncertainties. Effects of the pitch, roll and yaw were considered to correct the map. Map distortions are less than that shown in Fig. 12.

correlation coefficient between the grid maps of Figs. 12–15 with respect to the true map. It demonstrates that compensating for the effect of UUV motion considerably reduces distortion of the map. Additionally, taking into account both the uncertainties of the sidescan sonar and the uncertainties associated with the motion sensors of the UUV yields a resulting map that is much more accurate than that obtained with consideration of only the motion of the UUV. Fig. 18 plots the variation of target uncertainties with different slant ranges as well as the combination of sidescan sonarrelated uncertainty with UUV motion-related uncertainties. Figs. 19–23 show in detail the uncertainties associated with the sonar returns in different slant ranges. As mentioned before, the uncertainties associated with the sidescan sonar determine the resolution of the footprint. In Fig. 19, the solid ellipse represents the uncertainty of the target that is associated with the footprint of the sidescan sonar, and the uncertainty across the track exceeds that along the track. From Figs. 19 to 23, the solid ellipse becomes larger in the direction of the track and smaller in the across-track direction as the slant range is increased. Additionally, the dotted ellipse represents the combination of the uncertainty related to

0

50

100

150

200

250

300

350

400

450

meters Fig. 15. Grid map constructed by treating scanlines with uncertainties. Effects of the pitch, roll and yaw were considered to correct the map. Map distortions are less than those shown in Figs. 12–14. Most grid areas were covered by scanlines.

the sidescan sonar with those uncertainties related to the UUV motion. Since noise in yaw exceeds that in pitch and roll, the dotted ellipse becomes larger along the track with the slant range to a greater extent than it does across the track. The portside and starboard scanlines exhibit similar pattern in their uncertainties. We suggested that the shape of the target has less distortion by compounding uncertainties of the sidescan sonar with the UUV motion uncertainties. The application proposed here is that the UUV can use the target map as a navigation landmark to reset its position error. Such that the UUV can be operated in an area near the target for a long period of time by checking its position relative to the target periodically.

5. Conclusion This work presents a real-time method for mapping the sea floor using sidescan sonar for UUVs. The mapping system combines information on UUV motion with observations made using

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the sidescan sonar. The model based on observations made using the sidescan sonar elucidates the detection and localization of a target with respect to the UUV in global coordinates. Detection was conducted based on processing the sidescan sonar scanlines in real time. The target localization procedure locates the target and calculates the area of the footprint of the sidescan sonar. Finally, the occupancy grid mapping algorithm was applied to map the sea floor, combining the uncertainties of the motion of the UUV with measurements made using the sidescan sonar. Mapping techniques

proposed in this work could be applied to the self-localization of UUVs in unknown underwater environments that has distinct features detectable by scanlines of the UUV's sidescan sonar.

Uncertainties of Targets

0

Location of UUV

-10 1

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meters

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Along Track

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140

Fig. 18. Uncertainties of targets with different slant ranges in a scanline. This figure indicates the variation of targets uncertainties with different slant ranges and the uncertainties compounded by the side-scan sonar uncertainty with UUV motion uncertainties. The details of ranges 1–5 uncertainties are shown in Figs. 19–23.

160 50

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meters Fig. 16. True map constructed by multi-beam sonar.

1

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Target uncertainty relative to global coordinate Target uncertainty relative to global coordinate after compounding with UUV uncertainty

Targets -2

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meters 50

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Fig. 19. Uncertainties of target at range 1 near by the UUV of left scanline. The solid ellipse is the target uncertainties caused by the error of sidescan sonar. The dotted ellipse is the target uncertainties compounded by the sidescan sonar uncertainty with UUV motion uncertainties.

250

meters Fig. 17. Occupancy grid map constructed out of the true map.

Table 5 The grid map correlation with respect to the true map. Similarity to the true map is indicated by the value of the correlation coefficient. Figure number

UUV motion correction

Covariance compounded by the sidescan sonar uncertainty with UUV motion uncertainties

Map similarity correlation coefficient

Fig. 12 Fig. 13 Fig. 14 Fig. 15

No Yes No Yes

No No Yes Yes

0.248 0.306 0.302 0.417

E. Chen, J. Guo / Ocean Engineering 91 (2014) 252–262

261

5 Target uncertainty relative to global coordinate Target uncertainty relative to global coordinate after compounding with UUV uncertainty

-56.5 -57 -57.5

meters

-58 -58.5 -59 -59.5 -60 -60.5 -127

Fig. 20. Uncertainties of target at the range 2 of the left scanline.

Target uncertainty relative to global coordinate Target uncertainty relative to global coordinate after compounding with UUV uncertainty

-29

meters

-125 meters

-124

-123

-122

Fig. 23. Uncertainties of target at the range 5 of the left scanline.

Acknowledgment

3

-28.5

-126

The authors would like to thank the National Science Council Taiwan of the Republic of China, for financially supporting this research under Contract no. NSC95-3114-E002-001. The authors also thank Mr. Wei-Han Wang and Mr. Sheng-Wei Huang for their helps to this research. Anonymous reviewers are greatly appreciated for their comments and suggestions. References

-29.5

-30

-30.5 -63

-62.5

-62

-61.5

-61

-60.5

meters Fig. 21. Uncertainties of target in at the range 3 of the left scanline.

Fig. 22. Uncertainties of target at the range 4 in the left scanline.

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