发表机构
School of Computer Science, Wuhan University; School of Electrical and Electronic Engineering, Nanyang Technological University; School of Urban Design, Wuhan University; LIESMARS, Wuhan University（武汉大学计算机科学学院; 南洋理工大学电气与电子工程学院; 武汉大学城市设计学院; 武汉大学测绘遥感信息工程国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对森林环境中合并局部地图的难题，提出DTIF框架。通过提取树干地标、德劳内拓扑编码、统计筛选、一致性验证等步骤构建加权顶点对应，纳入可靠性权重到姿态估计器。实验证明该方法在边缘平台上兼顾鲁棒性、效率与可部署性，实现准确配准。
AI 中文摘要
准确的森林资源清查和大规模测绘对生态系统监测和可持续森林管理至关重要。多个低成本边缘平台能高效采集大面积数据，但在GNSS信号受阻的林下环境中合并独立构建的局部地图仍需无初始化的回环检测和全局配准。此任务具挑战性，因低成本激光雷达点云稀疏且有噪声，重复的树干布局和缺乏独特几何地标会导致严重的感知混淆和错误对应。为解决这些问题，我们提出DTIF，一个基于树干拓扑的轻量级框架用于森林回环检测和全局配准。先提取树干作为稳定地标并用德劳内拓扑编码以紧凑表示场景。然后用边长和半径统计筛选候选子地图，接着进行边-半径一致性验证和强弱顶点支持聚合以构建加权顶点对应。最后，将拓扑衍生的可靠性权重纳入解耦的稳健姿态估计器，在重力对齐下分别估计偏航、水平平移和高程平移。在模拟和真实森林数据集上的实验表明，DTIF以低计算开销实现了准确配准，在资源受限的边缘平台上的鲁棒性、效率和可部署性之间取得了良好平衡。
英文摘要
Accurate forest inventory and large-scale mapping are essential for ecosystem monitoring and sustainable forest management. Multiple low-cost edge platforms enable efficient large-area data acquisition, but merging independently constructed local maps in GNSS-denied understory environments still requires initialization-free loop closure detection and global registration. This task is challenging because low-cost LiDAR point clouds are sparse and noisy, while repetitive trunk layouts and the lack of distinctive geometric landmarks lead to severe perceptual aliasing and false correspondences. To address these issues, we propose DTIF (Delaunay Triangulation in Forests), a lightweight trunk-topology-based framework for forest loop closure detection and global registration. Tree trunks are first extracted as stable landmarks and encoded using a Delaunay topology for compact scene representation. Candidate submaps are then screened using edge-length and radius statistics, followed by edge--radius consistency verification and strong/weak vertex support aggregation to construct weighted vertex correspondences. Finally, topology-derived reliability weights are incorporated into a decoupled robust pose estimator that separately estimates yaw, horizontal translation, and elevation translation under gravity alignment. Experiments on simulated and real-world forest datasets demonstrate that DTIF achieves accurate registration with low computational overhead, providing a favorable balance among robustness, efficiency, and deployability on resource-constrained edge platforms.
Comments19 pages, 6 figures, 4 tables. Submitted to IEEE Transactions on Geoscience and Remote Sensing
