外观
论文arXiv 2609.173021 个月前
通过场景分解进行在线几何变化检测
原文链接：Online Geometric Change Detection via Scene Decomposition（arXiv 2609.17302 · 发布日期：2026-09-15）
Online Geometric Change Detection via Scene Decomposition
提出CDSD框架，用场景分解与子地图比较实现LiDAR或RGB-D在线几何变化检测，并在自采和开源数据集上验证。
核心要点
- 面向动态环境中的长期单次或多次任务，机器人需要识别倒树、开门等环境几何变化以辅助在线规划。
- CDSD将地图环境空间分解为独特场景，通过比较全局地图的稠密局部子地图来高效发现变化。
- 方法处理场景选择、代表性子地图生成、不同视场子地图比较，以及变化结果用于实时地图重建等挑战。
- 结果在马里兰Graces Quarters的Army Research Laboratory自采数据集和开源多会话变化检测数据集上展示。
验证范围与局限：验证范围为自采数据集和开源多会话变化检测数据集，摘要未说明真实机器人在线任务部署。
英文摘要 (Abstract)
Autonomous robots are increasingly deployed on long duration single- and multi-session missions in dynamic environments, where the ability to identify environmental changes such as fallen trees or opened doors provides important contextual information for online planning. We propose a framework called Change Detection via Scene Decomposition (CDSD) for accurate online geometric change detection using LiDAR or RGB-D sensors. Recent advances in geometric SLAM have made it possible to generate dense, tightly aligned maps without post processing, but comparing global maps across entire sessions is computationally expensive and does not allow for single-session online change detection. CDSD instead spatially decomposes mapped environments into unique scenes where changes can be found efficiently by comparing dense, local subsets of the global map called submaps. As the first submap-based approach for geometric change detection, we identify and address the following core challenges: 1) identifying appropriate scenes for change detection that require minimal redundant information; 2) generating dense and representative submaps for each scene; 3) detecting changes between submaps with differing fields of view; and 4) processing detected changes for real-time map reconstruction. Results demonstrate our algorithm on custom datasets collected at the Army Research Laboratory facility in Graces Quarters, Maryland, and on open-source multi-session change detection datasets.
作者：Thorne, David, Samuel Jia Cong Chua, Nakul Joshi, Wong, Aiden, Christa S. Robison, Philip R. Osteen, Brett T. Lopez
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