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*ently, the stereomodels cannot be tied *her to form a mosaic because of the *In CONCLUSIONS The task of mapping underwater features involves many “The first autochromes from the ocean bottom,” Nat. Geog. Mag., vol. 51, p. 56, (Jan.
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- Main Article Content
- Aquaculture & Marine Biology
- Optical Sensors and Methods for Underwater 3D Reconstruction
Hence, techniques such as structure from motion and stereo vision belong to both photogrammetric and computer vision communities. In photogrammetry, it is common to set up a camera in a large field looking at distant calibration targets whose exact location has been precomputed using surveying equipment. There are different categories for photogrammetric applications depending on the camera position and object distance.
For example, aerial photogrammetry is normally surveyed at a height of m [ ].
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On the other hand, close-range photogrammetry applies to objects ranging from 0. In a close-range setup, the cameras observe a specific volume where the object or area to reconstruct is totally or partially in view and has been covered with calibration targets.
The location of these targets can be known as before or calculated after the images have been captured if their shape and dimensions are known [ ]. Image quality is a very important topic in photogrammetry. One of the main important fields of this community is camera calibration, a topic that has already been introduced in Section 2. If absolute metric accuracy is required, it is imperative to pre-calibrate the cameras using one of the techniques previously mentioned and to use ground control points to pin down the reconstruction.
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This is particularly true for classic photogrammetry applications, where the reporting of precision is almost always considered mandatory [ ]. Underwater reconstructions can also be referred to as underwater photogrammetric reconstructions when they have a scale or dimension associated with the objects or pixels of the scene e.
According to Abdo et al. The most accurate way to recover structure and motion [ ] is to perform robust non-linear minimization of the measurement re-projection errors, which is commonly known in the photogrammetry communities as bundle adjustment [ 28 ]. Bundle adjustment is now the standard method of choice for most structure-from-motion problems and is commonly applied to problems with hundreds of weakly calibrated images and tens of thousands of points. In computer vision, it was first applied to the general structure from motion problem and then later specialized for panoramic image stitching [ 28 ].
Image stitching originated in the photogrammetry community, where more manually-intensive methods based on surveyed ground control points or manually registered tie points have long been used to register aerial photos into large-scale photo-mosaics [ 23 ]. The literature on image stitching dates back to work in the photogrammetry community in the s [ , ]. Zhukovsky et al. In [ 32 ], Menna et al. Photogrammetry is also performed by fusing data from diverse sensors, such as in [ ], where chemical sensors, a monocular camera and an MBS are fused in an archaeological investigation, and in [ ], where a multimodal topographic model of Panarea Island is obtained using a LiDAR, an MBS and a monocular camera.
Planning a photogrammetric network with the aim of obtaining a highly-accurate 3D object reconstruction is considered as a challenging design problem in vision metrology [ ]. The design of a photogrammetric network is the process of determining an imaging geometry that allows accurate 3D reconstruction.
There are very few examples of the use of a static deployment of cameras working as underwater photogrammetric networks [ ] because this type of approach is not readily adapted to such a dynamic and non-uniform environment [ ]. In [ ], de Jesus et al. They use a calibration prism composed of markers. Leurs et al. Different configurations to monocular or stereo camera systems have also been reported.
In [ ], Brauer et al. In [ ], Ekkel et al. They report an accuracy of 0.
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There exist different commercial solutions for gathering 3D data or to help with calculating it. In Table 7 , a selection of alternatives is shown. This system must be deployed underwater or fixed to a structure. The device samples 1 m 2 in 5 s at a 5-m range. SL1 is a similar device from 3D at Depth [ 67 ]. In fact, this company worked with Teledyne in this design [ ], and the specifications of these two pieces of equipment are quite close. It is produced by Smart light devices and uses a W green laser.
These are motorized solutions, so they must be deployed and static during their scan.
Aquaculture & Marine Biology
Savante provides three products. Cerberus [ ] is a triangulation sensor formed by a laser pointer and a receiver, capable of recovering 3D information. SLV [ ] is another triangulation sensor formed by a laser stripe and a high sensitivity camera, and finally, Lumeneye [ ] is a laser stripe that only casts laser light on the scene. Tritech provides similar to Savante a green laser sheet projector called SeaStripe [ ].
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The 3D reconstruction must be performed by the end-user camera and software. The selection of a 3D sensing system to be used in underwater applications is non-trivial. Basic aspects that should be considered are: 1 the payload volume, weight and power available, in case the system is an on-board platform, 2 the measurement time, 3 the budget and 4 the expected quality of the data gathered.
Optical Sensors and Methods for Underwater 3D Reconstruction
Regarding the quality, optical sensors are very sensitive to water turbidity and surface texture. Consequently, factors, such as the target dimensions, surface, shape or accessibility, may influence the choice and adaptiveness of the sensor to the reconstruction problem. Table 8 presents a comparison of the solutions surveyed in this article according to its typical operative range, resolution, ease of use, relative price and its suitability to be used on different platforms. Underwater 3D mapping has been historically carried out by means of acoustic multibeam sensors.
In that case, the information is normally gathered as an elevation map, and more recently, color and texture can be added afterwards from photo-mosaics, if available. In general, mono-propeller AUVs are not appropriate for optical imaging applications, because they cannot slow down their speed as required by the optical equipments. In some particular cases, even divers can be a choice. Optical mapping can also be accomplished with only SfM and, as industrial ROVs most often incorporate a video camera, it is feasible to record the needed images and reconstruct an entire scene see Campos et al.
However, these reconstructions lack a correct scale, and they are computationally demanding. If, instead, a stereo rig is used, SV techniques can be applied and can solve the scale problem. According to Bruno, SV is the easiest way to obtain the depth of a submarine scene [ 70 ]. These passive sensors are widely used because of their low cost and simplicity. Similarly to SfM, SV needs textured scenes to achieve satisfactory result, giving rise to missing parts corresponding to untextured regions in the final reconstruction.
To overcome the above-mentioned problems of SfM and SV and trying to increase the resulting resolution, SL uses light projection to cast features on the environment. These sensors are capable of working at short distances with high resolution, even for objects without texture. The drawback, compared to SV, is a slower acquisition time caused by the need to move the projection atop the scene or even to use different patterns. The acquisition time is a relevant problem that limits the use of SL systems in real conditions where the relative movement between the sensor and the scene can give rise to reconstruction errors.
In addition, acquiring data from dark objects using SL is, in general, strongly influenced by illumination and contrast conditions [ 70 ]. Shiny objects are also challenging for SL, because the reflected light may mislead the pattern decoder. Moreover, due to the large illuminated water volume, this technique is strongly affected by scattering, reducing its range. To minimize absorption, as well as common volume scattering, LbSL systems take advantage of selected wavelength sources in the green-blue region of the spectrum, extending their capable range. For an improved reduction of the scattering effects, the receiver window can be narrowed as in LLS sensors; even more, the emitter and the receiver can also be pulse gated [ 64 ], even though this strategy can be limited by a contrast decline.
On the other hand, when a precise and closer look at an object or structure is needed, LLS technology is not always suitable, as it has a large minimum measuring distance. Amongst optical solutions, laser-based sensors present a good trade-off between cost and accuracy, as well as an acceptable operational range. Accordingly, regarding the foreseeable future, more research on laser-based structured light and on laser line scanning underwater is needed.
These new devices should be able to scan while the sensor is moving, just like MBS, so software development and enhanced drivers are also required. Another challenge for the future is to develop imaging systems that can eliminate or reduce scattering while imaging. Solutions such as pulse gated cameras and laser emitters are effective [ ], but still expensive.
Overall, it is quite clear that no single optical imaging system fits all of the 3D reconstruction needs, covering very different ranges and resolutions. Besides, it is important to point out the lack of systematic studies to compare, with as much precision as possible, the performance of different sensors on the same scenario and conditions. One of these studies is authored by Roman et al. In that case, the stereo data showed less definition than the sonar and the SL.
MBS was captured during the laser survey at 5 Hz. As seen in these numbers, a different data rate induces less or more spatial resolution.
Nonetheless, Roman et al. Massot et al. Apart from other numerical details, the authors conclude that for survey missions, stereo data may be enough to recover the overall shape of the underwater environment, whenever there is enough texture and visibility. In contrast, when the mission is aimed at manipulation and precise measurements of reduced areas are needed, LbSL is a better option.
It would be advisable to work on similar approaches to the aforementioned for the near future, contributing to a better knowledge of each individual sensor behavior when used in diverse situations and applications and also to the progress in multisensor data integration methodologies. Table 9 summarizes the main strengths and weaknesses of the solutions surveyed in this article. The comments in the table are quite general, and a number of exceptions may exist.
With regard to the use of standard robots as data-gathering platforms, at present, scientists can mount their systems in the payload area, but in general, these systems are independent from the control architecture of the vehicle.