# Mobile Robots: Trajectory Analysis, Positioning and Control
## 2 The Present Issue
Furthermore, a veer chain matching scheme effectively corrects drift errors during turning maneuvers. Experimental evaluations on the KITTI odometry dataset demonstrate that this method achieves a balance between high computational efficiency and localization accuracy, with a real-time processing capability of 15 Hz on a single-thread CPU. The results underscore the potential of this approach in advancing robust and efficient visual odometry systems for mobile platforms. The authors of Contribution 8 present an advanced visual SLAM system that addresses the challenges of weak textures and complex geometries that are often encountered in industrial environments. The authors introduce a novel point-line-aware heterogeneous graph attention network (HAGNN) to improve the robustness and accuracy of feature extraction and matching. Their system integrates a point-line geometric feature extraction network (PL-Net) with an attention mechanism that aggregates the contextual features of points and lines for enhanced SLAM performance. To optimize feature matching, the study transforms the matching process into an optimal transport problem, solved using a Greedy Inexact Proximal Point Method (GIPOT). This approach reduces computational complexity while achieving optimal feature assignments. Experiments conducted on the KITTI dataset and a custom industrial dataset demonstrated significant improvements in pose estimation accuracy and robustness compared to state-of-the-art SLAM algorithms like ORB-SLAM2. Additionally, the system's performance was validated in real-world scenarios, such as a virtual simulation of oil and gas station inspections, where it achieved high consistency with ground truth trajectories. The authors conclude that the integration of point-line features with advanced attention mechanisms significantly enhances SLAM system reliability in challenging environments. They also highlight the potential for future improvements, such as by incorporating semantic information to handle dynamic objects. The authors of Contribution 9 propose a novel spatial location representation method for mobile robots inspired by mammalian spatial cognition mechanisms. This approach addresses the challenges of low localization accuracy and large cumulative errors in long-term navigation within unknown environments. The authors introduce a system that integrates boundary, grid, and place cells, modeled after the navigation cells in mammalian brains, to improve environmental perception and localization accuracy. The method incorporates boundary information by modeling the firing characteristics of boundary cells based on direction- and distance-aware data relative to environmental boundaries. These boundary cell responses are input into a Location-Adaptive Hierarchical Network (LAHN), which generates grid cells and updates their distribution to better align with environmental boundaries. The system also utilizes competitive Hebbian learning to produce place cells, ensuring precise location representation. The experimental results demonstrate that the proposed method effectively reduces localization errors by correcting cumulative drift through boundary cell activation. The spatial representation maps generated show high accuracy in dynamic and complex environments, highlighting the method's potential for enhancing mobile robot navigation and environmental cognition. The authors of Contribution 10 address the challenge of dual-user operation in remote robot systems equipped with force feedback, where two users collaboratively control a single robot to perform tasks such as object transportation. Their work focuses on clarifying the mechanisms by which a user with lower network delay can assist another user with higher delay, as well as proposing enhanced robot position control to improve efficiency and reduce the force exerted on objects during operation.