Problem
Research questions and friction points this paper is trying to address.
LiDAR Bundle Adjustment
Large-scale Mapping
Parallel Computing
Computational Efficiency
Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing
🤖 AI Summary
This study addresses the computational inefficiency of bundle adjustment in large-scale LiDAR mapping by proposing the first fully parallelized acceleration framework for this task. The method innovatively integrates adaptive asynchronous GPU data loading, parallelizable bottom-up voxel feature extraction, and a Majorization-Minimization optimization strategy, alongside a novel parallel incremental solver to overcome serial processing bottlenecks. Extensive experiments on multiple large-scale datasets demonstrate that the proposed framework achieves up to a tenfold improvement in computational efficiency while maintaining mapping accuracy comparable to state-of-the-art methods. These results significantly advance the feasibility of real-time applications in large-scale LiDAR SLAM systems.
📝 Abstract
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 computation, including the computation of residuals, Jacobian and Hessian matrices, and a parallel increment solver. To support our design, we provide both theoretical and experimental analysis of the time complexity of our approach. Extensive benchmarking on large-scale public datasets across various computational platforms validates the robustness and adaptability of our approach, achieving up to a tenfold improvement in computational efficiency while preserving mapping accuracy comparable to state-of-the-art methods. To benefit future research, the implementation code is available on GitHub.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Parallel Bundle Adjustment
Asynchronous Data Loading
Bottom-up Voxelization
Majorization-Minimization
LiDAR Mapping
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