This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a neural network can predict the class given the generated point cloud. By reconstructing the 3D shape of the object, the proposed method can classify the object accurately through a straightforward training process. We verified the proposed method by performing simulations and field experiments.

Three-dimensional reconstruction is a crucial technique for mapping and object-search tasks, but it is challenging in sonar imaging because of the nature of acoustics. In underwater sensing, many advanced studies have introduced approaches that have included feature-based methods and multiple imaging at different locations. However, most existing methods are prone to environmental conditions, and they are not adequate for continuous data acquisition on moving autonomous underwater vehicles (AUVs). This paper proposes a sensor fusion method for 3D reconstruction using acoustic sonar data with two sonar devices that provide complementary features. The forward-looking multibeam sonar (FLS) is an imaging sonar capable of short-range scanning with a high horizontal resolution, and the profiling sonar (PS) is capable of middle-range scanning with high reliability in vertical information. Using both sonars, which have different data acquisition planes and times, we propose a probabilistic sensor fusion method. First, we extract the region of interest from the background and develop a sonar measurement model. Thereafter, we utilize the likelihood field generated by the PS and estimate the elevation ambiguity using importance sampling. We also present the evaluation of our method in a ray-tracing-based sonar simulation environment and the generation of the pointclouds. The experimental results indicate that the proposed method can provide a better accuracy than that of the conventional method. Because of the improved accuracy of the generated pointclouds, this method can be expanded for pointcloud-based mapping and classification methods.

This paper proposes a multi-sonar fusion-based underwater three-dimensional (3D) point cloud generation method for optimal scan path planning of an autonomous underwater vehicle (AUV). 3D reconstruction based on forward-looking sonar (FLS), a crucial sensor due to its robustness in underwater environments, is essential for underwater mapping and scan path planning of AUVs. A multi-sonar fusion method using FLS and profiling sonar (PS) in a previous study can eliminate the false slope points generated due to noise and the ambiguity in the vertical field-of-view (FoV) of the FLS. However, it shows limitations depending on “the encountered yaw angle”, defined as the angle between AUV’s scan path direction and the front edge line of object’s upper area, which cannot be easily accounted for using a two-dimensional (2D) PS map. The proposed method detects the encountered yaw angle in the current scan path and estimates the next optimal scan path where the encountered yaw angle is suitable for fusion. Subsequently, using the fused 3D reconstruction result from the optimal scan path, we propose a 3D reconstruction of PS data for fusion with the FLS. We evaluated our method in the simulator and water tank experiments. The results show that the proposed method surpasses other methods, FLS only and the conventional fusion method with the FLS and 2D PS map in both environments. Finally, the proposed method will contribute to applications such as underwater exploration and AUV navigation, which require accurate mapping and scan path planning.

This paper proposes a method to accurately measure underwater electric field signals induced by underwater objects for the precise localization of the objects. We devised a mechanical system that calibrates an underwater electric field sensor (EFS) to measure the 3-D components of the electric field signals and three-dimensionally localize the underwater objects with the measurements. To calibrate the EFS, it was installed underwater wherein a reference electric field was configured, and the electric field signals were measured. The misalignment among the axes of the EFS was approximated by comparing the electric field measurements and theoretical calculations under similar measurement conditions. The electric field measurements were then calibrated to the signals to be measured with an ideally structured EFS without axis misalignment. The proposed system was tested through computer simulations and water-tank experiments. The results indicate that the proposed system can estimate the axial misalignment of the EFS and calibrate its electric field measurements. Furthermore, to test the effectiveness of the proposed calibration of the EFS, electric-field-based localization of an underwater electric source was conducted. The localization became more accurate after the application of the proposed calibration method. The proposed system will contribute to missions such as underwater exploration, construction, and surveillance, which require accurate 3-D localization of underwater objects.