Fusion of Swath Bathymetric Data: Application to AUV Rapid Environment Assessment

International audienceBuilding an accurate and large digital terrain model (DTM) of the seabed is a key issue in various applications, especially for covert rapid environment assessment using autonomous underwater vehicles (AUVs). New AUV generations are capable of acquiring bathymetry with multiple acoustic sensors: single-beam echosounder, Doppler velocity log, multibeam echosounder (MBES), interferometric sidescan sonar (ISSS), etc. As these sensors acquire the seabed with different geometries, they can be combined to produce a DTM in shorter time. For example, ISSS can reach a wide swath in shallow water but it shows an information gap at nadir, which is usually covered by completing an additional track. Simultaneously using the MBES to acquire the nadir removes the need for this additional track and reduces the energy consumption of the whole survey. This paper focuses on fusion algorithms to extract best information of the two sensors (MBES and ISSS) to feed DTM production software with optimal bathymetric information. This problem may be solved by taking into account the average information of the two sensors. However, the sensors do not always give accurate information and the average information therefore becomes biased. Another way to tackle the problem is to select a priori information given by the better of the two sensors based on a given geometric parameter (e.g., grazing angle). In this case, when the assumed best sensor fails, the information produced by the second sensor cannot be used to compensate for the erroneous information. Our approach consists in using all the available information and fuses it ahead of producing the DTM. Based on the theory of belief functions, this paper presents a framework to fuse the information coming from the two swath bathymetric sensors (MBES and ISSS). The belief theory, applied successfully to other fields, has been extended to handle the bathymetric information. The reliability and the uncertainty of each sonar are introduced in the fusion process to improve the estimation and the accuracy of the final terrain model. First, simulated sonar data, with perfectly known ground truth, are used to quantitatively assess the performance of the fusion process by comparing DTM obtained with and without fusion. Then, the experimental validation is conducted on actual data, acquired simultaneously by the two sonars systems (Klein K5000 ISSS, Reson 8125 MBES) mounted on the DAURADE AUV. Our evaluation of the fusion method shows significant quantitative and qualitative improvement in the production of the final DTM