Graph Optimality-Aware Stochastic LiDAR Bundle Adjustment With Progressive Spatial Smoothing
Year: 2025
Type: article
Abstract: Large-scale LiDAR Bundle Adjustment (LBA) to refine sensor orientation and point cloud accuracy simultaneously for building navigation maps is a fundamental task in logistics, intelligent transportation, and robotics. In the context of autonomous delivery and smart mobility, the 3D map obtained by accurate and robust LBA plays a pivotal role in enabling reliable localization and navigation across complex, large-scale urban environments. 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 achieverobust,efficient, andscalableLBA. The Progressive Spatial Smoothing (PSS) module extractsrobustLiDAR 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 anefficientoptimization. GOSO then utilizes stochastic clustering and graph marginalization to solve the large-scale state estimation problem for ascalableLBA. We validate PSS-GOSO across diverse scenes captured by various platforms, demonstrating its superior performance compared to existing methods. Moreover, the resulting point cloud maps are used for automatic last-mile delivery in large-scale complex scenes, showcasing the practical benefits of our method in modern intelligent transportation systems. The project page can be found at:https://kafeiyin00.github.io/PSS-GOSO/
Authors: Jianping Li, Thien-Minh Nguyen, Muqing Cao, Shenghai Yuan, Tzu-Yi Hung, Lihua Xie
Institutions: Nanyang Technological University, Carnegie Mellon University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413318925