Visual simultaneous localization and mapping (VSLAM) is invaluable in applications like autonomous driving, underground robot exploration, and robot search and rescue operations. However, traditional VSLAM struggles in dim dynamic environments, prompting researchers to develop specialized algorithms for such environments. Despite these efforts, many existing VSLAM algorithms often fail to meet real-time requirements.

In this study, a real-time VSLAM algorithm for dim dynamic scenes is proposed. First, this study uses an algorithm based on the Retinex theory to enhance the brightness of input red green blue(RGB) images. Because images captured by the red green blue-depth(RGB-D) cameras cannot directly obtain illumination maps, this study transforms the illumination solving problem into an optimization problem. By developing an illumination cost function and taking its derivative, the derivative is yielded as zero, and a photometric graph is obtained. Second, this paper adds an object detection thread on top of ORB-SLAM3 using YOLO to identify targets labeled “person.” Next, feature points in the YOLO detection frame are prefiltered using the Lucas-Kanade(LK) optical flow method. Further, the position of the feature point in the next frame is predicted using this method. If the distance between the actual and predicted positions is greater than the preset threshold, the feature point is set as a dynamic feature point, and the other feature points are set as static feature points. Additionally, Gaussian distribution analysis is used for secondary judgment. By analyzing the standard deviation of the depth of feature points in the YOLO box, the variance and standard deviation ratio of their depth that are greater than and less than the standard deviation are calculated, and the distribution of feature points is estimated. These metrics are compared against thresholds, and different thresholds are selected for subsequent standardized scores. Finally, the standardized score for each preset static feature point is calculated to evaluate the degree of deviation from the original data point, yielding a mean of 0 and a standard deviation of 1. In static scenes, the depth values of most feature points should be relatively consistent, and even if there are some deep changes, these changes should be distributed around a mean. The depth value of dynamic feature points may significantly deviate from the mean value of static feature points in the boundary box, resulting in higher standardization scores and larger square values than the set thresholds. Through this judgment method, all dynamic feature points are successfully selected and culled.

Test results on the TUM and Bonn public datasets show that the proposed algorithm reduces the absolute trajectory error by at least 86.93% in high dynamic environments and 27.61% in low dynamic environments compared with ORB-SLAM3. Among similar VSLAM algorithm types, the proposed algorithm offers faster processing speeds while maintaining high precision and achieves a good balance between accuracy and real-time performance. The actual scene verification shows the excellent performance of the proposed algorithm in dim scenes. In summary, through advanced image processing and accurate dynamic point culling, the proposed algorithm addresses the shortcomings of traditional VSLAM algorithms and achieves accurate positioning in dim dynamic environments.

To address the challenges of complex underwater environments, particularly the uncertainties in ocean currents, this study proposes an improved sparrow search algorithm (ISSA) for autonomous underwater vehicles (AUVs) path planning. The goal is to enhance the efficiency and robustness of path planning by minimizing navigation time and improving path stability in uncertain conditions.

The proposed ISSA incorporates several key enhancements to the classic sparrow search algorithm (SSA). First, a vector analysis method is developed to evaluate interval responses, allowing the algorithm to effectively handle uncertainties in ocean currents. By modeling the uncertain ocean currents as intervals, the algorithm can accurately calculate the energy consumption and navigation times for different paths. Second, the ISSA introduces segmented learning and quantum mechanisms to improve global search capabilities. These mechanisms enable the algorithm to dynamically adjust its search strategy by learning from both elite and marginal individuals within the population, thereby enhancing diversity and preventing premature convergence. Third, a Cauchy-Gaussian mechanism is integrated into the update formula to balance global exploration and local exploitation during the search process. Finally, the population size is dynamically updated using Thompson sampling, allowing the algorithm to adaptively allocate computational resources based on the complexity of the environment.

Simulation results demonstrate that the ISSA significantly outperforms the original SSA and other state-of-the-art algorithms, such as particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), and whale optimization algorithm (WOA). Specifically, the ISSA reduces the average maximum navigation time by 49.88% compared to the original SSA and decreases the failure rate in extreme ocean current conditions by 10.6%. The ISSA also exhibits superior convergence speed, achieving near-optimal paths in approximately 20 iterations, while other algorithms require around 40 iterations to approach the global optimum. Moreover, the ISSA shows a lower average fitness value, indicating better optimization performance and path planning efficiency.

The ISSA demonstrates strong global search capabilities and robustness in dynamic and uncertain underwater environments, making it a promising solution for AUV path planning. The improvements in convergence characteristics and the ability to handle complex ocean currents highlight the algorithm's potential for practical applications. Future work will focus on further optimizing the computational efficiency of ISSA and exploring its application in more diverse and challenging underwater scenarios.