Abstract
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.
Highlights
Object classification by autonomous underwater vehicles (AUVs) is necessary for various underwater missions, such as terrain-based navigation [1,2], target detection [3], and surveillance [4].Because sonar sensors are robust to turbidity and have a long sensing range, AUVs are commonly equipped with a sonar sensor to perceive surrounding objects [5,6,7]
By analyzing the captured sonar images as the AUV passes over an object, the 3D geometry of the underwater scene is restored in a point cloud
We confirmed the precision of the point cloud and the accuracy of the Neural networks (NNs) by classifying the generated test point clouds with the pre-trained PointNet
Summary
Object classification by autonomous underwater vehicles (AUVs) is necessary for various underwater missions, such as terrain-based navigation [1,2], target detection [3], and surveillance [4]. Various algorithms that can accurately classify objects even in noisy and low-resolution sonar data have been developed. Because open-source sonar data are scarce, underwater experiments should be conducted to capture training sonar images of objects according to viewing angles, which would require significant time and cost. Lee et al [16] proposed an NN that classifies objects using eight sonar images taken at 45-degree intervals while rotating around the objects To tackle the challenges of underwater object classification based on 2D sonar images, we propose a method to classify underwater objects by reconstructing their three-dimensional (3D). The proposed method restores lost elevation information from consecutive sonar images and classifies the objects based on the reconstructed 3D geometry of the objects.
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