Establishing the correspondences between newly acquired points and historically accumulated data (i.e., map) through nearest neighbors search is crucial in numerous robotic applications.However, static tree data structures are inadequate to handle large and dynamically growing maps in real-time.To address this issue, we present the i-Octree, a dynamic octree data structure that supports both fast nearest neighbor search and real-time dynamic updates, such as point insertion, deletion, and on-tree down-sampling. The i-Octree is built upon a leaf-based octree and has two key features: a local spatially continuous storing strategy that allows for fast access to points while minimizing memory usage, and local on-tree updates that significantly reduce computation time compared to existing static or dynamic tree structures.The experiments show that i-Octree surpasses state-of-the-art methods by reducing run-time by over 50% on real-world open datasets.

翻译：通过最近邻搜索建立新获取点与历史积累数据（即地图）之间的对应关系，在众多机器人应用中至关重要。然而，静态树数据结构难以满足实时处理大规模且动态增长地图的需求。为解决此问题，我们提出i-Octree——一种支持快速最近邻搜索和实时动态更新（如点插入、删除及树上降采样）的动态八叉树数据结构。i-Octree基于叶节点八叉树构建，具有两个关键特性：局部空间连续存储策略，可在最小化内存占用的同时实现快速点访问；以及局部树上更新机制，相比现有静态或动态树结构，能显著降低计算耗时。实验表明，在真实世界开放数据集上，i-Octree将运行时间降低超过50%，超越了当前最先进方法。