In modern urban environments, visual sensors are crucial for enhancing the functionality of navigation systems, particularly for devices designed for visually impaired individuals. The high-resolution images captured by these sensors form the basis for understanding the surrounding environment and identifying key landmarks. However, the core challenge in the semantic segmentation of blind roads lies in the effective extraction of global context and edge features. Most existing methods rely on Convolutional Neural Networks (CNNs), whose inherent inductive biases limit their ability to capture global context and accurately detect discontinuous features such as gaps and obstructions in blind roads. To overcome these limitations, we introduce Dual-Branch Swin-CNN Net(DSC-Net), a new method that integrates the global modeling capabilities of the Swin-Transformer with the CNN-based U-Net architecture. This combination allows for the hierarchical extraction of both fine and coarse features. First, the Spatial Blending Module (SBM) mitigates blurring of target information caused by object occlusion to enhance accuracy. The hybrid attention module (HAM), embedded within the Inverted Residual Module (IRM), sharpens the detection of blind road boundaries, while the IRM improves the speed of network processing. In tests on a specialized dataset designed for blind road semantic segmentation in real-world scenarios, our method achieved an impressive mIoU of 97.72%. Additionally, it demonstrated exceptional performance on other public datasets.

Enabling rapid and accurate comprehensive environmental perception for vehicles poses a major challenge. Object detection and monocular distance estimation are the two main technologies, though they are often used separately. Thus, it is necessary to strengthen and optimize the interaction between them. Vehicle motion or object occlusions can cause sudden variations in the positions or sizes of detection boxes within temporal data, leading to fluctuations in distance estimates. So, we propose a method to integrate a detector based on YOLOv5-RedeCa, a Bot-Sort tracker and an anomaly jumping change filter. This combination allows for more accurate detection and tracking of objects. The anomaly jump filter smooths distance variations caused by sudden changes in detection box sizes. Our method increases accuracy while reducing computational demands, showing outstanding performance on several datasets. Notably, on the KITTI dataset, the standard deviation of the continuous ranging results remains consistently low, especially in scenarios with multiple object occlusions or disappearances. These results validate our method’s effectiveness and precision in managing dual tasks.

Mapless autonomous navigation in constrained pedestrian environments requires mobile robots to transform local semantic and geometric perception into physically executable motion commands. This paper presents a semantic-geometric fusion navigation framework for a quadruped robot operating on tactile paving with static and dynamic obstacles. A morphology-based topological reconstruction module converts visual semantic masks into continuous centerline references without relying on pre-built maps, while also improving robustness to local fractures and noise. Visual tactile-paving trajectories and LiDAR obstacle observations are then projected into a unified robot body frame, providing a metric representation for local planning. Based on this representation, a task-specific nonlinear model predictive control (NMPC) scheme integrates semantic path tracking, geometric obstacle avoidance, angular-velocity response approximation, command smoothing, and execution-layer safety filtering. Experiments on a Unitree Go2 quadruped robot equipped with RGB-D and LiDAR sensors demonstrate improved path coverage, gap-repair accuracy, collision-free traversal, and reduced command oscillation compared with conventional path extraction and local planning baselines. The results show that the proposed framework improves the robustness and executability of quadruped navigation in tactile-paving environments.