Mehul Sangekar

Development of a landing algorithm for autonomous underwater vehicles using laser profiling

The autonomous underwater vehicle has proven to be a useful tool for ocean research. However detailed seafloor observations such as microscopic analysis of sand grain structure or study of microbial colonies require a platform with stable footing on the seafloor, which cannot be provided by a cruising type AUV. In this research the authors propose a new class of AUV, capable of landing to provide a stable, but mobile, platform with which to perform these observations. This paper proposes a sensing system and a software algorithm to enable the AUVs to perform landing. A light sectioning based method is used to scan the seafloor with high resolution. Since the seafloor can change abruptly and at short intervals, the reliability and functioning of such technology requires real-time seafloor classification and detection of suitable landing sites. A landing algorithm has been developed which uses three dimensional bathymetry data and calculates a landing vector coordinate in real-time. A microscope sensor payload developed to obtain magnified images of the seafloor after landing has also been tested. Data from sea experiments are presented, where the algorithm demonstrated real-time generation of landing vector coordinates for an ROV.

Instruments and Methods for Acoustic and Visual Survey of Manganese Crusts

This paper describes acoustic and visual instruments developed to perform high-resolution surveys of the volumetric distribution of manganese crusts from an underwater vehicle. The instruments consist of an acoustic device, developed to perform in situ measurements of manganese crust thickness at depths of up to 3000 m, and a vision-based mapping system that generates 3-D color reconstructions of the seafloor. Methods to process the information obtained by these sensors to automatically identify areas of exposed crust using the 3-D reconstructions, and subsequently determine the thickness of the crusts based on the acoustic measurements, are described. Sea trials were performed at #5 Takuyo seamount with the systems mounted onboard the remotely operated vehicle Hyper-Dolphin during the NT10-11 cruise of the R/V Natsushima. The results are that the first time in situ measurements of manganese crust thickness have been performed, and it is demonstrated that, for the types of substrate dominant in the surveyed area, continuous acoustic measurement of manganese crust thickness is possible. The work described in this paper indicates that the proposed instruments and data processing algorithms can form useful tools to enable more efficient survey of manganese crusts.

Pixel based mapping using a sheet laser and camera for generation of coloured 3D seafloor reconstructions

There are several approaches for 3 dimensional mapping of the seafloor in the actual colours, many of which require multiple cameras, elaborate algorithms and specially designed vehicles. In this paper a method is presented, which uses minimal equipment and simple algorithms for this task, while treating the data in a fully 3 dimensional way from input to output. Because of the reduced hardware demands, it is well suited for combined missions, where the underwater vehicle records some other data as primary task and the map created with the proposed method acts as a support for visualising that data and the environment where it was collected.

Pixel mapping for generation of 3D coloured seafloor bathymetry using a single camera

Visual surveys of the seafloor are often performed by robotic underwater vehicles and a number of methods exists for transforming photos or video into 2D or 3D reconstructions, most of which utilise navigation data. Laser profiling is a method that is widely used for bathymetry mapping with high vertical resolution, but typically no information of the actual colour is obtained. In this paper we propose a method for high resolution three dimensional mapping of the seafloor in colour based on laser profiling and retrieval of colour information through pixel based mapping. It employs a compact algorithm treating the data in a fully three dimensional way from input to output, which does not yield visual artifacts usually encountered when employing methods assuming planar scenes. The same hardware as for laser profiling (one video camera, a sheet laser and a navigation sensor), plus a lamp are sufficient for recording the required data. The sensors require only little space, allowing the system to be used in missions where other data is collected as the main task. Combining the 3D map with that data would allow the information to be plotted as a geographic information system (GIS) in the future.

Wide area seafloor observation using an autonomous landing vehicle with adaptive resolution capability

Underwater vehicles are currently being extensively used for observation and study of the seafloor. Detailed seafloor analysis requires wide area observations with high resolution information obtained from in-situ analysis from sensors or by sampling. Certain sensors require high altitude and high scanning where as others need proximity to the seafloor or contact with stable footing to perform integrated measurements over a period of time. Such wide area high resolution surveys cannot be performed by a single cruising or hovering type vehicle and often requires multiple deployments of different types of vehicles. In this research the authors have designed and developed a new class of AUV along with an intelligent survey technique in which an underwater vehicle can generate centimeter order wide area maps of the seafloor by cruising at high speeds using an acoustic sensor. At intermediate locations of interest identified by processing the acoustic data autonomously during the mission, higher order resolution information can be obtained by lowering scanning speed and altitude using a laser profiling system. The vehicle can also land at some of these locations to obtain integrated measurements at the same position or magnified images. A slightly negatively buoyant underwater vehicle has been designed and developed with systems necessary to perform such a survey. An algorithm to detect areas of interest from acoustic scanned bathymetry has been implemented and tested. An autonomous landing system has been developed which uses laser scanned bathymetry to calculate a landing vector coordinate for safe landing on the seafloor. Experiments were conducted at a tank facility to demonstrate the multi-resolution survey scheme. An artificially generated seafloor scenario was acoustically scanned from a high altitude and speed to identify an area of interest in real-time. This area was then scanned at an higher resolution by lowering altitude and speed to perform autonomous landing by detecting suitable landing sites using the landing algorithm. The results from these experiments have been included in the paper.

Autonomous landing experiments with an underwater vehicle for multi-resolution wide area seafloor observation

The autonomous underwater vehicle has proven to be an important tool for study of the seafloor. Detailed seafloor analysis often requires wide area observations with high resolution information. Certain sensors require close proximity to the seafloor or contact, with stable footing to perform integrated measurements over a period of time. Such wide area high resolution surveys cannot be performed by a cruising or hovering type vehicle alone. In this research the authors propose a new class of AUV along with a survey technique in which an underwater vehicle can generate meter order resolution wide area maps of the seafloor, but at intermediate locations, obtain higher, centimeter order resolution information by lowering scanning speed and altitude and finally, by landing to obtain micrometer order resolution measurements or to perform integrated measurements at the same position. A new underwater vehicle with slight negative buoyancy has been developed which has hardware and software to perform landing on the seafloor. Since the seafloor can change abruptly and at short intervals, the reliability and functioning of such technology requires real-time seafloor classification for detection of suitable landing sites. A landing algorithm has been developed which uses laser profile data to calculate a landing vector coordinate for safe landing in realtime and this has been implemented on a newly developed landing vehicle. An autonomous landing system has been developed which uses this algorithm to perform landing operations. Experiments were conducted at a tank facility to demonstrate real-time computation of the landing algorithm and autonomous landing of the vehicle using the proposed system. Results from the landing experiments conducted are presented in this paper.

Identification of manganese crusts in 3D visual reconstructions to filter geo-registered acoustic sub-surface measurements

The volumetric distribution of cobalt-rich manganese crusts (CRC) is of significant interest for mining and geology. Traditionally studying underwater deposits of CRC involved physical sampling from Remotely Operated Vehicles (ROV) or using dredges. Recently, acoustic measurements of crust thickness have been demonstrated that can give a significantly higher spatial resolution for measurement. The probe makes high resolution measurements of sub-surface reflections to calculate the thickness of the deposit. However, CRC coverage is often not continuous and it is difficult to determine from the acoustic signals alone whether an acoustic signal was measured over CRC or not. The authors propose a method to filter these using visual information (3D colour map of the seafloor) from the same region. After locating the acoustic beam on the seafloor, points around the region are selected. A number of analyses are performed on this point cloud to extract parameters that can reliably discriminate between exposed CRC and other types of seafloor. The proposed method was tested on two areas of seafloor - one which is known to contain crust and one which does not contain crust. These two sets of data were then used to train a Support Vector Machine (SVM) classifier. The trained classifier was then tested with the training sets and a test set containing both crust and non-crust regions to verify if the CRC is being detected reliably. The results were promising; in 85.4% of the cases, the detection was successful. The performance will be verified by using larger sets of data. In our future work, the results can be applied to estimate a volumetric distribution of CRC in the region.

Automatic Extraction of Thickness Information from Sub-Surface Acoustic Measurements of Manganese Crusts

Mapping and estimating the volumetric distribution of cobalt-rich manganese crusts (Mn-crust) is a challenging task that lies at the centre of deep-sea mineral prospecting. Acoustic methods are effective and capable of in-situ continuous measurements of Mn-crust thickness, providing much higher spatial resolutions compared to traditional methods involving sampling. However, processing acoustic signal in order to estimate thickness values is difficult due to low signal to noise ratios. This paper proposes a combination of image processing techniques in addition to acoustic signal processing in order to improve the accuracy of measurements. The advantage is the possibility of using the physical properties of Mn-crust, such as local continuity in order to recognize valid measurements. Testing the algorithm on data collected from sea experiments demonstrate that the reflected signals from the crust can be identified, resulting in spatially continuous thickness estimates.

Acoustic and visual survey of manganese crusts using an underwater vehicle at #5 Takuyo seamount

This paper describes acoustic and visual instruments developed to perform high resolution surveys of manganese crusts. The instruments consist of an acoustic device developed to perform in situ measurements of manganese crust thickness at depths of up to 3000m, and a vision based mapping system that generates three-dimensional colour reconstructions of the sea floor. The developed systems operate at low altitudes, using an underwater platform such as an autonomous underwater vehicle (AUV) or remotely operated vehicle (ROV) to maintain a close range to the sea floor. In this communication, the authors describe the acoustic probe and vision based mapping system together with the algorithms that have been implemented to process the data they obtain, and present some results from sea trials performed using the ROV Hyper-Dolphin of JAMSTEC during the NT10–11 cruise of the R/V Natsushima at #5 Takuyo seamount. During the survey, the ROV was operated at depths of between 1000 and 3000 m at low altitudes of <1.0m, with surge velocities of between 0.4 to 0.6 knots to survey the area both acoustically and visually. The results are the first time in situ measurements of manganese crust thickness have been successfully performed, and demonstrate that the proposed instruments are useful tools to enable more efficient surveys of manganese crusts.