外观
论文arXiv 2608.142662 个月前
通过并行计算加速 LiDAR 建图的大规模束调整
原文链接：Accelerating Large-Scale Bundle Adjustment for LiDAR Mapping via Parallel Computing（arXiv 2608.14266 · 发布日期：2026-08-01）
Accelerating Large-Scale Bundle Adjustment for LiDAR Mapping via Parallel Computing
提出全并行计算框架，加速大规模 LiDAR 建图的束调整，并在公开数据集和多平台验证。
核心要点
- LiDAR 束调整用于构建全局一致点云地图，但大规模建图计算与显存压力突出。
- 框架结合异步自适应数据加载、自底向上体素化平面特征提取与并行优化。
- 在大规模公开数据集和多种平台基准测试中，效率最高提升十倍且精度相当。
- 作者给出时间复杂度的理论与实验分析，并在 GitHub 开源实现代码。
验证范围与局限：验证限于大规模公开数据集和多种计算平台的基准测试。
英文摘要 (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.
作者：Yixi Cai, Rundong Li, Qingwen Zhang, Yuhan Xie, Patric Jensfelt, Fu Zhang
来源
本页事实对应的公开页面，以原文为准。