Fig 1.
The main steps are swath correction, in charge of improving the individual SSS swaths; probabilistic map building, whose task is to generate a probability map estimating the likelihood of each pixel to be observed; echo intensity map building, which constructs a grid where each cell holds the most likely echo intensity corresponding to that cell; and localization, whose task is to estimate the AUV pose. These steps are not executed sequantially, but they are sensor-driven.
Fig 2.
Side-scan sonar characterization.
The AUV navigates at an altitude h with two SSS sensing heads symmetrically placed on port and starboard with a fixed angle θ. The angles α and ϕ are known as the sensor openings and model the sound expansion rate in the YZ and XY planes respectively. A point p in the ensonified region is usually expressed in polar coordinates using its slant range rs and its grazing angle θs, though sometimes the point altitude hp is also used. Given this information, the point position can be expressed as a range rg over the Y axis. Finally, due to the ϕ opening, the exact position of a point in the XY plane cannot be determined. Instead, the point can be anywhere over the arc q.
Fig 3.
An example of a single swath corresponding to the port (bins 1 to 250) and starboard (bins 251 to 500) sensing heads of a particular SSS. The Y axis shows the obtained echo intensities normalized to values between 0 and 1. The central region with low echo intensities corresponds to the so-called blind zone and the first significant echo outside the blind zone is known as the First Bottom Return (FBR).
Fig 4.
Example of acoustic image showing consecutively gathered swaths. Each column corresponds to a single swath and the echo intensities are mapped to a grayscale where black denotes no echo received and white represents the maximum echo intensity.
Fig 5.
The problems of the flat floor assumption.
(a) Given a measured slant range rs and AUV altitude h, if the height hp of the object responsible for the echo is unknown and assumed to be zero following the flat floor assumption, an error e appears in the estimated ground range rg. This error is defined as the difference between the actual ground range and the one obtained if the detected object is assumed to have zero height. (b) Given a slant range rs and knowing the angular sensor placement θ and opening α the true object height must be in the interval [hp, min, hp, max].
Algorithm 1.
Slant correction.
Fig 6.
Each map cell or pixel represents a squared area of δM × δM meters. Each cell can be represented by the coordinates q0, q1, q2, q3 of its four vertexes. The terms and
denote the angle with respect to the AUV reference frame of the rightmost and leftmost vertexes of a particular cell as observed from the AUV. The terms
and
denote the minimum and maximum ground range, respectively, that the SSS can reach.
Fig 7.
A pixel may not be observed because it lies outside the maximum measurement range (q0) or because, due to the AUV motion, it lies between two observed regions (q1). A pixel may be observed by a single measurement (q2) or by several measurements (q3).
Algorithm 2.
Probabilistic layer building.
Fig 8.
A polygonal mesh is used to perform the extrapolation.
Each polygon pi in the mesh is constructed by joining the acoustic axes of the measurements mi and mi+1.
Table 1.
Parameters of the SSS used in this paper.
Fig 9.
Trajectory followed by the AUV (red line) overlayed to a Google Maps view of the environment.
Fig 10.
(a) Error model as a function of the unknown object height and the measured slant range. The error is computed using Eq 3 and represents the difference, in meters, between the actual ground range corresponding to a detected object, and the estimated ground range if the flat floor assumption is performed. (b) Fully neglectable errors. The errors in (a) that are below the sensor resolution are depicted here and considered neglectable.
Fig 11.
Structure with the largest shadow in our dataset.
The values rs1 = 10.1m and rs2 = 14.5m have been found by manually selecting the shadow. This example is used to determine the maximum ground range error emax in our dataset due to the flat floor assumption. This maximum error is 0.7m and is not fully neglectable, but it leads only to a 1.16% of misplacement with respect to the whole SSS range.
Table 2.
Flat floor assumption summary.
Fig 12.
Estimated ensonification intensities according to the proposed model.
(a) Intensities corresponding to a single swath involving both port and starboard sensing heads. The intensities have been normalized to a value between 0 and 1. (b) Intensities corresponding to a transect. The swath axis denotes consecutively gathered swaths.
Fig 13.
Reflectivity corresponding to a single swath.
The image shows the swath in Fig 3 corrected according to the model in Eq 12.
Fig 14.
The columns correspond to consecutively gathered swaths, either corrected or not. (a) Original. (b) Slant correction. (c) Slant correction + Blind zone removal. (d) Slant correction + Blind zone removal + Intensity correction. (e) Ensonification intensity model. In this case, each column corresponds to the model output for each swath.
Fig 15.
Example of the improvements due to the swath correction.
(a) Raw SSS data showing a distorted straight underwater pipe. (b) Corrected data. The pipe image is corrected to its actual straight shape. The uneven brightness is also improved due to the intensity correction process. In both cases a red line is overlayed to emphasize the geometric improvement.
Fig 16.
SSS probabilistic models of two sensing heads.
The parameters used are rg, min = 2.5m, rg, max = 10m, ϕ = 45o and δM = 0.05m. The picture shows the observation probabilities involving a single SSS measurement according to (a) Gaussian PDF (), (b) Triangular PDF (
) and (c) Uniform PDF (
). The gray levels have been re-scaled to 0–1 for the sake of clarity.
Table 3.
Time spent by ,
and
to build the example images in Fig 16.
Fig 17.
Probability layer MP of a region of 180m x 90m with significant overlapping between swaths.
The resolution δM is 30cm. (a) Gaussian approach . (b) Triangular approach
. (c) Uniform approach
.
Fig 18.
Probability layer MP of region of 15m x 15m with significant overlapping between swaths.
The resolution δM is 5cm. (a) Gaussian approach . (b) Triangular approach
. (c) Uniform approach
.
Table 4.
Summary of the time consumption.
Fig 19.
(a) Region of 750m x 270m using a resolution δM = 30cm. (b) Region of 30m x 20m using a resolution δM = 30cm corresponding to the area inside the red rectangle in a). (c) The same region in b) using a resolution δM = 12cm. (d) Region of 5.7m x 3.5m using a resolution δM = 12cm corresponding to the area inside the red rectalngle in c). (e) The same region in d) using a resolution δM = 5cm. (f) Raw SSS data.
Fig 20.
Example of data from the same sea-floor region gathered from different viewpoints.
(a) Data gathered with the AUV moving south-east. (b) Data gathered with the AUV moving north-west. (c) Resulting intensity map fusing the overlapping data. The resolution is δM = 12cm and the mapped area is 24mx10m.
Fig 21.
Example of the echo intensity map built using the presented approach.
(a) Raw SSS data. (b) Echo intensity map MI with a resolution δM = 0.09m.
Table 5.
Summary of the time consumption.
Fig 22.
Some cells may remain unobserved due to the AUV motion.
This situation corresponds to the pixel q1 in Fig 7. (a) Visual gaps due to unobserved map cells. (b) Gaps filled with the Geometric map approach.
Fig 23.
The geometric approach can also be used standalone to build the maps.
This figure shows two examples build with (a) a resolution δM = 12cm and (b) a resolution δM = 5cm.
Fig 24.
Probabilistic and intensity maps overlayed.
The probabilistic shows the probability of each cell to be observed, ranging from blue (low probability) to red (high probability). The echo intensity layer shows the estimated sea floor structure.