ECO动态构建3D滑动窗口，实时高效处理点云流。
ECO: Incremental Ego-Centric Octree Update for Point Streams
- 以机器人为中心构建动态3D滑窗，避免全局坐标变换
- 相比全重建平均提速24.87%，生成体素图快34.17%
- 自然保留移动物体短期记忆，适合实时感知系统
为移动机器人实时处理连续点云流构建八叉树面临显著的计算与内存挑战。传统全局结构常导致高延迟和树结构失衡。本文提出自适应中心八叉树（ECO），作为3D滑动窗口，动态限制建图空间至机器人周围区域。ECO采用高效的增量更新算法，将环境划分为移出、移入和重叠区域，消除冗余全局坐标变换。在KITTI基准上的评估表明，相比完整静态重建，更新时间最多降低25.60%（平均24.87%）；相比有界增量基线，最多降低67.52%（平均54.60%）。此外，ECO显著降低下游任务总延迟，在体素地图生成中比完整重建快达34.17%。在动态场景中，ECO自然保留移动物体的短期时序记忆，提供有用上下文，同时控制更新成本并保持树结构平衡，适用于实时空间感知。
原文摘要 · Abstract (English)
Constructing octrees for mobile robots that process continuous point streams in real time poses significant computational and memory challenges. Standard global structures often suffer from high latency and unbalanced tree growth. We introduce the Ego-Centric Octree (ECO), a spatial data structure that acts as a 3D sliding window, dynamically bounding the mapping space to the robot's immediate surroundings. ECO uses an efficient incremental update algorithm that categorizes the environment into shift-out, shift-in, and overlap regions, eliminating redundant global coordinate transformations. Evaluations on the KITTI benchmark demonstrate that ECO reduces update times by up to 25.60% (24.87% on average) compared to full static reconstruction and by up to 67.52% (54.60% on average) compared to a bounded incremental baseline. Furthermore, ECO substantially lowers the total system latency of downstream tasks, running up to 34.17% faster than full reconstruction in voxel-map generation. In dynamic scenes, ECO naturally retains a short-term temporal memory of moving objects, providing useful temporal context while keeping update cost bounded and the tree balanced for real-time spatial perception.
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