Graph Optimality-Aware Stochastic LiDAR Bundle Adjustment with Progressive Spatial Smoothing

Large-scale LiDAR Bundle Adjustment (LBA) for refining sensor orientation and point cloud accuracy simultaneously is a fundamental task in photogrammetry and robotics, particularly as low-cost 3D sensors are increasingly used for 3D mapping in complex scenes. Unlike pose-graph-based methods that rely solely on pairwise relationships between LiDAR frames, LBA leverages raw LiDAR correspondences to achieve more precise results, especially when initial pose estimates are unreliable for low-cost sensors. However, existing LBA methods face challenges such as simplistic planar correspondences, extensive observations, and dense normal matrices in the least-squares problem, which limit robustness, efficiency, and scalability. To address these issues, we propose a Graph Optimality-aware Stochastic Optimization scheme with Progressive Spatial Smoothing, namely PSS-GOSO, to achieve \textit{robust}, \textit{efficient}, and \textit{scalable} LBA. The Progressive Spatial Smoothing (PSS) module extracts \textit{robust} LiDAR feature association exploiting the prior structure information obtained by the polynomial smooth kernel. The Graph Optimality-aware Stochastic Optimization (GOSO) module first sparsifies the graph according to optimality for an \textit{efficient} optimization. GOSO then utilizes stochastic clustering and graph marginalization to solve the large-scale state estimation problem for a \textit{scalable} LBA. We validate PSS-GOSO across diverse scenes captured by various platforms, demonstrating its superior performance compared to existing methods.