A Survey of Underwater Acoustic SLAM System

Jiang, Min; Song, Sanming; Li, Yiping; Jin, Wenming; Liu, Jian; Feng, Xisheng

doi:10.1007/978-3-030-27532-7_14

Cited by 12 publications

(7 citation statements)

References 46 publications

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“…Furthermore, there are other sonar systems such as synthetic aperture sonar (SAS) [55] and dual-frequency identification sonar (DIDSON) [56]. A comprehensive survey of sonar SLAM can be found in [57][58][59].…”

Section: • Multibeam Sonarmentioning

confidence: 99%

An Overview of Key SLAM Technologies for Underwater Scenes

et al. 2023

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Autonomous localization and navigation, as an essential research area in robotics, has a broad scope of applications in various scenarios. To widen the utilization environment and augment domain expertise, simultaneous localization and mapping (SLAM) in underwater environments has recently become a popular topic for researchers. This paper examines the key SLAM technologies for underwater vehicles and provides an in-depth discussion on the research background, existing methods, challenges, application domains, and future trends of underwater SLAM. It is not only a comprehensive literature review on underwater SLAM, but also a systematic introduction to the theoretical framework of underwater SLAM. The aim of this paper is to assist researchers in gaining a better understanding of the system structure and development status of underwater SLAM, and to provide a feasible approach to tackle the underwater SLAM problem.

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Section: • Multibeam Sonarmentioning

confidence: 99%

An Overview of Key SLAM Technologies for Underwater Scenes

et al. 2023

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“…In recent years, SLAM systems have garnered increased attention as a means of addressing AUV navigation issues, with two main categories: sonar-based and camera-based underwater SLAM [4]. As sound travels further underwater than electromagnetic waves, sonar-based SLAM is widely used in AUV navigation [5]. Mallios et al [6] successfully performed a 600 m long underwater SLAM experiment on a pier in an artificially structured environment, using a scanning imaging sonar as a sensor and obtaining the desired results.…”

Section: Introductionmentioning

confidence: 99%

An effective stereo SLAM with high-level primitives in underwater environment

Xu¹,

Ma²,

Li³

et al. 2023

Meas. Sci. Technol.

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Visual simultaneous localization and mapping (SLAM) algorithms face challenges in complex underwater scenarios, such as turbidity, dynamism, and low texture, where point features are unreliable and can lead to weakened or even failed systems. To overcome these issues, high-level object features are considered due to their accuracy and robustness. In this paper, we introduce an effective object-level SLAM method that employs a stereo camera to enhance the navigation robustness of autonomous underwater vehicles and generates a detailed semantic map. Point features and object features are integrated to serve the proposed approach. We begin by detecting 2D objects in images using a state-of-the-art neural network, followed by obtaining 3D objects described by the general model through the principle of multi-view geometry and eventually constructing semantic landmarks. To account for object data association, we present an object match method that takes into consideration the stereo camera characteristics in a single stereo frame and a filter-based approach to track the landmarks in odometry. Experiments are also conducted using the KITTI dataset and our sequences collected from the pool and coast. The evaluation results indicate that the proposed method can improve the performance of ORBSLAM2 in terms of both navigation robustness and mapping information in underwater scenarios.

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“…SLAM needs sensors that can explore the external environment. The sensors used in underwater SLAM mainly include the camera, laser scanner, side-scan sonar, multi-beam sounder, and forward-looking sonar [1,2]. An amount of research has been focused on the above SLAM technology.…”

Section: Introductionmentioning

confidence: 99%

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Guan

Zhou

et al. 2022

JMSE

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Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on forward-looking sonar for AUV. The algorithm uses an extended Kalman filter (EKF) to estimate the AUV motion states. First, the SLAM method fuses the data coming from the navigation sensors to predict the motion states. Subsequently, a novel particle swarm optimization genetic algorithm (PSO-GA) scan matching method is employed for matching the sonar scan data and grid map, and the matching pose would be used to correct the prediction states. Lastly, the estimated motion states and sonar scan data would be used to update the grid map. The experimental results based on the field data have validated that the proposed SLAM algorithm is adaptable to underwater conditions, and accurate enough to use for ocean engineering practical applications.

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A Survey of Underwater Acoustic SLAM System

Cited by 12 publications

References 46 publications

An Overview of Key SLAM Technologies for Underwater Scenes

An Overview of Key SLAM Technologies for Underwater Scenes

An effective stereo SLAM with high-level primitives in underwater environment

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

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