Tunnel-SLAM: Low-Cost LiDAR/Vision/RTK/Inertial Integration on Vehicles for Roadway Tunnels

Reliable positioning and mapping in roadway tunnels are crucial for vehicle-based monitoring and inspection, especially considering the challenging environmental conditions such as rapidly changing illumination, low-texture environments, and repetitive structural elements. While general LiDAR-inertial odometry (LIO) frameworks and loop-closure detection methods are effective in general scenarios, they often suffer from severe drift or incorrect loop constraints under these specific conditions. These challenges are further exacerbated by the inherent uncertainties associated with low-cost sensors. This paper introduces a narrow field-of-view LiDAR-centric RTK-visual-inertial SLAM system enhanced by three key modules: semantic-assisted loop detection and matching, two-stage RTK quality control, and adaptive factor graph optimization (FGO). In the first module, the proposed semantic loop descriptor (SLD) matching is used to determine the potential loop closure locations and then integrates the corresponding constraint as graph nodes. The quality control module addresses RTK outlier rejection during tunnel entry and exit, employing an event-driven stochastic model to characterize the uncertainty between RTK and the other sensors, effectively suppressing RTK-induced errors. FGO module performs optimization by incorporating LIO, RTK, and loop closure factors, employing a keyframe-based strategy to produce globally optimized poses while continuously updating the map. The proposed Tunnel-SLAM was evaluated against state-of-the-art SLAM algorithms in four extended roadway tunnels, ranging in traveling distance approximately from 5 to 10 km. Experimental results demonstrate that the proposed SLAM achieved a final drift of less than 2 m with loop closure, demonstrating significantly reducing the drift, while other existing SLAM frameworks fail catastrophically or have large drift.