Robust Multi-Modal Factor Graph Optimization for Distributed Collaborative LiDAR–Visual–Inertial SLAM

To address accuracy and reliability challenges in simultaneous localization and mapping (SLAM) systems under extreme conditions, this paper presents LIVE-SLAM, a tightly-coupled LiDAR–inertial–visual framework. The technical core integrates a LiDAR Probabilistic Feature Extraction (LPFE) module to reduce frontend overhead by retaining high-confidence features, an adaptive confidence-based weighting strategy in the backend optimization to dynamically balance multi-modal residuals during sensor degradation, and a Visual Redundancy Removal (VRR) based hybrid loop closure mechanism to mitigate perceptual aliasing. Evaluation on the KITTI benchmark and challenging real-world datasets demonstrates that our multi-sensor fusion effectively prevents tracking failures typical of single-sensor systems. Specifically, compared to the LVI-SAM framework, the frontend runtime is reduced by 49% and backend efficiency is improved by 25% in complex urban sequences. Furthermore, our approach achieves an average RMSE improvement of 35.3% over FAST-LIO2 and LIO-SAM in diverse real-world scenarios, particularly in environments with geometric degradation and lighting variations. These findings confirm the system’s superior real-time efficiency and global localization precision in both standard benchmarks and complex practical applications.