Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing
LiDAR bundle adjustment is widely utilized in mapping to construct globally consistent point cloud maps. In this paper, we propose the first fully parallel computing framework to accelerate LiDAR bundle adjustment for large-scale mapping, incorporating three key techniques. First, we design an adaptive, asynchronous data loading strategy to efficiently process large-scale point cloud datasets on memory-constrained GPUs. Secondly, we present a novel bottom-up voxelization method for extracting planar features, enabling fully parallelized pre-processing. Thirdly, we build upon a majorization-minimization formulation to accelerate compute-intensive tasks in the optimization via parallel comp...
| Authors | Yixi Cai, Rundong Li, Yuhan Xie et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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