Various underwater studies using underwater sonar sensors are actively in progress. However, unlike the ground, the underwater has a lot of noise. So it is difficult to accurately recognize the underwater environment. The final purpose of this study is to improve the efficiency of the underwater environment recognition using the underwater sonar sensor by developing a filtering algorithm that removes noise and expresses the object from the underwater sonar image captured by the underwater sonar sensor. To develop a filtering algorithm, convolutional calculations were used with three types of filters. This paper is about the case study that conducted to set the parameters of ‘Gabor Filter’ suitable for underwater sonar image during the design process of filtering algorithm. As a result, it was possible to find the most suitable ‘Gabor Filter’ parameters for underwater sonar images. And it showed high accuracy with a binary map of obstacles created by hand using the naked eye. Through this study, it can be utilized not only as a binary map of real-time obstacles, but also as an algorithm for generating object masks in underwater sonar images for deep learning.

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re3). Re3 has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.

In this paper we present (1) analysis of imaging sonar measurement for two-view relative pose estimation of an autonomous vehicle and (2) bundle adjustment and 3D reconstruction method using imaging sonar. Sonar has been a popular sensor for underwater application due to its robustness to water turbidity and visibility in water medium. While vision based motion estimation has been applied to many ground vehicles for motion estimation and 3D reconstruction, imaging sonar addresses challenges in relative sensor frame motion. We focus on the fact that the sonar measurement inherently poses ambiguity in its measurement. This paper illustrates the source of the ambiguity in sonar measurements and summarizes assumptions for sonar based robot navigation. For validation, we synthetically generated underwater seafloor with varying complexity to analyze the error in the motion estimation.

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.