Accurate team positioning in GNSS-denied environments is essential for military, rescue, and industrial operations. This paper presents a mutual registration-based collaborative algorithm for inertial navigation systems that eliminates reliance on external infrastructure. Specifically, an accelerometer bias estimation-based zero-velocity update (ABE-ZUPT) algorithm is introduced to mitigate velocity drifts and enable precise dead reckoning. Additionally, when ultrawideband (UWB) ranging and position information sharing are conducted among some members, mutual registration is performed to correct the accumulated errors in heading and position estimation. Field experiments in urban road areas with multiple members demonstrate that our algorithm achieves a significant improvement in positioning accuracy over standalone and the conventional EKF-based cooperative methods: In a 1,115-meter long-distance test (Trial 1), collaborative positioning reduced closure errors by 87.17% (28.91 m → 3.71 m) and average errors by 67.57% (19.09 m → 6.19 m) compared to standalone estimation. For complex maneuvers (Trial 2, a 107-m circular path with rapid turns), we achieved a 50% reduction in closure error (1.30 m → 0.65 m) and a 29.45% average error reduction (1.46 m → 1.03 m). Additionally, we conducted experimental validation of anti-interference performance and member scalability, achieving favorable results. These validate our method as a robust solution for high-precision collaborative positioning in GNSS-denied environments.

Collaborative geometric reconstruction of building structures can significantly reduce communication consumption for data sharing, protect privacy, and provide support for large-scale robot application management. In recent years, geometric reconstruction of building structures has been partially studied, but there is a lack of alignment fusion studies for multi-UAV (Unmanned Aerial Vehicle)-reconstructed geometric structure models. The vertices and edges of geometric structure models are sparse, and existing methods face challenges such as low feature extraction efficiency and substantial data requirements when processing sparse graph structures after geometrization. To address these challenges, this paper proposes an efficient deep graph matching registration framework that effectively integrates interpretable feature extraction with network training. Specifically, we first extract multidimensional local properties of nodes by combining geometric features with complex network features. Next, we construct a lightweight graph neural network, named EKNet, to enhance feature representation capabilities, enabling improved performance in low-overlap registration scenarios. Finally, through feature matching and discrimination modules, we effectively eliminate incorrect pairings and enhance accuracy. Experiments demonstrate that the proposed method achieves a 27.28% improvement in registration speed compared to traditional GCN (Graph Convolutional Neural Networks) and an 80.66% increase in registration accuracy over the suboptimal method. The method exhibits strong robustness in registration for scenes with high noise and low overlap rates. Additionally, we construct a standardized geometric point cloud registration dataset.

With the development of intelligent unmanned systems, it is important for indoor mobile mapping and structural perception to reconstruct building structures in a timely manner from sequential LiDAR point clouds. However, many existing reconstruction methods rely on complete or accumulated point clouds, making them less suitable for partial observations, occlusions, and continuous updates. This paper proposes an incremental geometric reconstruction framework for building structures based on LiDAR point clouds. The method combines temporal state inheritance and orthogonal projection to transform 3D point-cloud processing into 2D plane-based contour updating. A transmissive relationship-based hole detection strategy is introduced to preserve real openings such as doors and windows while completing partially unobserved regions. Simulation and real-world experiments show that the proposed method can recover major planar building structures. In the simulation scene, the proposed method achieves a CD-L1 of 0.088 m, a CD-L2 of 0.007 m², and an F1-score of 0.901, with an average single-frame processing time of 1.02 s. The experimental results indicate that the proposed method provides a compact and interpretable plane-based structural representation for near-real-time incremental reconstruction of building structures from sequential LiDAR point clouds.