To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior information from the optical flow to estimate relative pose transformation information. In cases where there is poor optical flow tracking and only a small number of key points successfully match, the feature point method with a filtering mechanism is used for posing estimation. By coupling and correlating the two aforementioned methods, this visual odometry greatly accelerates the computation time for relative pose estimation. It reduces the computation time of relative pose estimation to 40% of that of the ORB_SLAM3 front-end odometry, while ensuring that it is not too different from the ORB_SLAM3 front-end odometry in terms of accuracy and robustness. The effectiveness of this method was validated and analyzed using the EUROC dataset within the ORB_SLAM3 open-source framework. The experimental results serve as supporting evidence for the efficacy of the proposed approach.

Efficient path planning and trajectory tracking are central to the safe and autonomous navigation of autonomous underwater vehicles (AUVs) in complex and unknown environments. To address the inherent challenges of safety, smoothness, and exploration efficiency in such settings, this paper presents an integrated framework that synergistically couples three enhanced core modules with complementary innovations. First, improved I-LazyTheta* and A-IRRT* algorithms are developed to incorporate safety margin collision detection and dynamic obstacle avoidance weight regulation, which enable efficient generation of collision-free paths that proactively maintain safe clearance in cluttered 3D spaces. Second, a trajectory tracking module based on a finite-state machine is designed, integrating B-spline optimization and a curvature-adaptive speed control mechanism to ensure high-precision following with guaranteed path smoothness and trackability. Third, a novel 3D autonomous exploration strategy tailored to underwater sonar constraints is constructed, combining frontier point clustering, multi-dimensional information gain evaluation, and traveling salesman problem (TSP) path optimization to achieve efficient unknown environment traversal while significantly reducing redundant detection and energy consumption. The proposed framework supports modular decoupling for independent reuse as well as integrated collaborative operation, offering flexible adaptability to diverse underwater robotic platforms. Simulations demonstrate that the integrated approach achieves superior performance in path safety and tracking accuracy, along with an exploration coverage of 79.08%, validating its effectiveness for robust AUV autonomy in complex underwater scenarios.

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness consistency recovery method is pro-posed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from the images. After image-level processing, a combined underwater acousto-optic feature association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for the visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for performance against the OKVIS framework. The system achieves better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

Point cloud classification and part segmentation require effective downsampling methods that reduce computational cost while preserving object boundaries, thin structures, and other informative geometric regions. However, raw point clouds are often large, irregular, and non-uniformly distributed, making efficient downsampling essential. This paper proposes the Curvature-Aware Sampling Network (CASNet), a feature-preserving framework that combines curvature-aware sampling, inverse-density weighting, attention pooling, and residual blocks. CASNet estimates local curvature from neighborhood covariance and converts curvature scores into sampling probabilities, increasing the likelihood of retaining geometrically informative points. Inverse-density weighting further reduces redundant selection in dense regions and improves the representation of sparse but structurally important areas. Although motivated by point-wise semantic prediction, the proposed sampling and aggregation modules are task-independent. Accordingly, ModelNet40 classification evaluates global geometric discrimination, ShapeNet Part provides direct point-wise segmentation evaluation, and Semantic3D and S3DIS examine sampling behavior in complex scenes. Under the controlled hierarchical abstraction backbone, the CAS + IDW sampling configuration achieves 92.26% instance accuracy on ModelNet40 and 85.41% instance-average mIoU on ShapeNet Part. In the architecture-level evaluation, the complete CASNet obtains 94.30% overall accuracy and 91.70% mean class accuracy on ModelNet40, together with 85.29% class-average mIoU and 87.61% instance-average mIoU on ShapeNet Part. With only 64 retained points, CAS + IDW achieves 53.86% classification accuracy, compared with 53.65% for curvature-informed FPS, 53.37% for SAMBLE, 51.92% for FPS, and 32.64% for random sampling. In the controlled sampling experiment, CAS + IDW requires 90 s per training epoch and 49 ms per sample for inference, compared with 103 s and 59 ms for the FPS-based counterpart, respectively. These results demonstrate that CASNet maintains competitive predictive performance while providing favorable computational efficiency.

Efficient path planning and trajectory tracking are central to the safe and autonomous navigation of autonomous underwater vehicles (AUVs) in complex and unknown environments. In this paper, we propose an integrated framework that couples enhanced path planning, curvature-adaptive trajectory tracking, and sonar-constrained 3D exploration. First, the path planner is improved by incorporating safety margin-based collision detection, 3D obstacle avoidance weights, and online replanning. Second, the tracking module is enhanced with B-spline optimization and curvature-adaptive speed control to ensure smooth and dynamically feasible trajectories. Third, the exploration strategy is augmented with frontier clustering, multi-dimensional information gain evaluation, and TSP path optimization. Our framework jointly addresses practical constraints including forward-looking sonar field-of-view limitations, safety clearance margins, and the coupling of dynamic replanning with low-level tracking feasibility, while supporting both modular decoupling and integrated collaborative operation. Simulations using ArduSub SITL and Gazebo demonstrate that our integrated approach achieves a superior performance in path safety and tracking accuracy, along with an exploration coverage of 79.08%, validating its effectiveness for robust AUV autonomy in unknown 3D underwater scenarios.