For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

Multi-beam sonar imaging detection technology is increasingly becoming the mainstream technology in fields such as hydraulic safety inspection and underwater target detection due to its ability to generate clearer images under low-visibility conditions. However, during the multi-beam sonar detection process, issues such as low image resolution and blurred imaging edges lead to decreased target segmentation accuracy. Traditional filtering methods for echo signals cannot effectively solve these problems. To address these challenges, this paper introduces, for the first time, a multi-beam sonar dataset against the background of simulated crack detection for dam safety. This dataset included simulated cracks detected by multi-beam sonar from various angles. The width of the cracks ranged from 3 cm to 9 cm, and the length ranged from 0.2 m to 1.5 m. In addition, this paper proposes a BS-UNet semantic segmentation algorithm. The Swin-UNet model incorporates a dual-layer routing attention mechanism to enhance the accuracy of sonar image detail segmentation. Furthermore, an online convolutional reparameterization structure was added to the output end of the model to improve the model’s capability to represent image features. Comparisons of the BS-UNet model with commonly used semantic segmentation models on the multi-beam sonar dataset consistently demonstrated the BS-UNet model’s superior performance, as it improved semantic segmentation evaluation metrics such as Precision and IoU by around 0.03 compared to the Swin-UNet model. In conclusion, BS-UNet can effectively be applied in multi-beam sonar image segmentation tasks.

The inner-surface damage of water conveyance tunnels is the main hidden danger that threatens their safety and leads to serious accidents. The method based on the principle of acoustic reflection is the main means of inspecting damage to water-conveyance tunnels. However, affected by the tunnel environment and equipment noise, the obtained acoustic point cloud model inevitably suffers from noise, which can produce erroneous results. Therefore, we proposed a novel filtering method, called unsharp-mask-guided filtering for 3D point cloud, to reduce the impact of noise on the acoustic point cloud model of water-conveyance tunnels. The proposed method fuses the ideas of guided filtering and the unsharp masking technique and extends them to the 3D point cloud model by considering the position of the point. In addition, edge-aware weighting mean is also used to retain the edge features of the point cloud model while smoothing the noise points. The experimental results show that our method can obtain impressive results and a better performance in both the acoustic point cloud model of the tunnel and the simulated point cloud model than many state-of-the-art methods.