发表机构
KTH Royal Institute of Technology; the University of Hong Kong（瑞典皇家理工学院; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出首个全并行计算框架，结合三项关键技术加速激光雷达建图的大规模光束平差法，经多平台公开数据集测试，效率最高提升10倍且精度相当，代码已开源。
AI 中文摘要
激光雷达光束平差法广泛应用于建图领域，用于构建全局一致的点云地图。本文提出首个用于加速大规模建图中激光雷达光束平差法的全并行计算框架，包含三项关键技术：其一，设计自适应异步数据加载策略，以在内存受限的GPU上高效处理大规模点云数据集；其二，提出一种新型自底向上体素化方法，用于提取平面特征，实现全并行预处理；其三，基于 majorization-minimization 公式，通过并行计算加速优化过程中的计算密集型任务，包括残差、雅可比矩阵、海森矩阵的计算，以及并行增量求解器。为支撑所提设计，本文对所提方法的时间复杂度进行了理论与实验分析。在各类计算平台上对大规模公开数据集开展的大量基准测试，验证了所提方法的鲁棒性与适应性，其计算效率最高提升10倍，同时保持与现有最优方法相当的建图精度。为助力未来研究，实现代码已在GitHub上开源。
英文摘要
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.
CommentsAccepted by IEEE International Conference on Automation Science and Engineering (CASE), 2026
