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-min
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- LinkedLinked via arxiv author · 85%Yixi Cai →
“Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing”
- LinkedLinked via arxiv author · 85%Rundong Li →
“Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing”
- LinkedLinked via arxiv author · 85%Yuhan Xie →
“Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing”
- LinkedLinked via arxiv author · 85%Qingwen Zhang →
“Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing”
- LinkedLinked via arxiv author · 85%Patric Jensfelt →
“Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing”
- LinkedLinked via arxiv author · 85%Fu Zhang →
“Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing”