An Overview of Key SLAM Technologies for Underwater Scenes

Wang, Xiaotian; Fan, Xiaopeng; Shi, Pengfei; Ni, Jianjun; Zhou, Zhongkai

doi:10.3390/rs15102496

Cited by 24 publications

(9 citation statements)

References 142 publications

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“…Feature extraction algorithms are used to identify salient points in images or key features in point clouds. Then, matching feature points in adjacent frames are computed and the geometric relationship is exploited to obtain the rotation matrix and translation matrix of the camera [7].…”

Section: Location Creationmentioning

confidence: 99%

Simulation Analysis of Fire-Fighting Path Planning Based On SLAM

Jing

2024

HSET

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Reasonable and optimal planning of fire fighting paths is crucial to improving the efficiency of fire fighting, and has attracted increasing research attention in recent years. The traditional fire path selection method is mainly to locate the hot spot of the fire scene, and at the same time, use the hot spot sensor to locate the path correction algorithm to realize the planning of the fire path. Thanks to the rapid development of artificial intelligence algorithms, the fire extinguishing path planning and disaster relief resource scheduling methods based on artificial intelligence algorithms have continuously made breakthroughs in accuracy and speed. Based on simultaneous localization and mapping (SLAM) technology, this paper solves the positioning and mapping problems simultaneously to determine the optimal path. Specifically, this paper mainly uses the map construction and navigation functions of SLAM, and uses Gazebo to simulate an open shopping mall environment and a factory environment with multiple obstacles. Through simulation experiments, the results verify the feasibility of this proposed method in fire evacuation path planning, and can realize the evacuation path planning in case of fire.

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Section: Location Creationmentioning

confidence: 99%

Simulation Analysis of Fire-Fighting Path Planning Based On SLAM

Jing

2024

HSET

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“…There is currently a significant increase in the interest around the examination of underwater robotic platforms, which have a wide range of practical uses. The combination of visual technology with underwater robotic platforms has the potential to accelerate technological progress in both fields, consequently increasing their societal usefulness [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning

Wang,

Zhao

et al. 2024

Biomimetics

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Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments.

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“…Traditional point cloud preprocessing methods are mainly based on the Euclidean clustering method, which relies on feature information such as shape and texture [16,17].…”

Section: Introductionmentioning

confidence: 99%

A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot

Yang,

Chen,

Liu

et al. 2023

Forests

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Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 m2, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots.

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An Overview of Key SLAM Technologies for Underwater Scenes

Cited by 24 publications

References 142 publications

Simulation Analysis of Fire-Fighting Path Planning Based On SLAM

Simulation Analysis of Fire-Fighting Path Planning Based On SLAM

A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning

A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot

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