MSR-SLAM: A Multi-Stage Map-Refinement Framework for Reliable LiDAR–Inertial SLAM in GPS-Denied Environments

LiDAR–inertial odometry can remain locally accurate while accumulating unbounded global drift, and in structurally repetitive GPS-denied environments, the loop closures intended to remove that drift may instead corrupt the map. This paper presents MSR-SLAM, a multi-stage map-refinement framework built on an existing LiDAR–inertial odometry front end. Loop candidates retrieved by a learned place-recognition descriptor are verified geometrically. In the offline batch configuration, geometrically verified loop constraints are incorporated into pose graph optimization after the complete sequence is available. In the online configuration, geometrically verified candidates pass through an additional admission stage that rejects constraints whose candidate-induced translational or rotational correction exceeds preset limits before they can affect the active pose graph. The global map is subsequently reconstructed from the optimized poses. The main real-robot evaluation comprised seven offline/online processing cases derived from five distinct recorded traversals in indoor, outdoor, and subterranean environments, using a Unitree Go2 quadruped carrying a 16-beam LiDAR. Additional live traversals were used separately for the online consistency analysis. In one online sequence, 14 of the 15 geometrically verified candidates were rejected at the admission stage; in a loop-free tunnel, all 310 retrieved candidates were rejected during geometric verification, and the trajectory was left unmodified. The offline failure case E4 combined excellent registration quality and large residual reduction with severe global distortion and no loop edge spanning 500 or more node indices, highlighting the need to assess loop-edge coverage alongside local fit. Three independent traversals of one environment produced geometric acceptance rates between 10.8% and 11.2%, with exactly one loop constraint admitted in each traversal. On KITTI Sequence 00, MSR-SLAM reduced the RKO-LIO ATE RMSE from 9.5525 m to 4.9322 m (48.4%) while leaving the short-term RPE essentially unchanged.