发表机构
University of California, Los Angeles; DEVCOM Army Research Laboratory (ARL)（加州大学洛杉矶分校; DEVCOM陆军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CDSD框架，通过将环境分解为场景并比较子地图，实现基于LiDAR或RGB-D的在线几何变化检测，解决计算效率与实时性问题，并在自建及公开数据集上验证。
AI 中文摘要
自主机器人越来越多地被部署在动态环境中的长时间单次和多次任务中，在这些环境中，识别诸如倒下的树木或打开的门等环境变化的能力为在线规划提供了重要的上下文信息。我们提出了一种名为“通过场景分解进行变化检测”（CDSD）的框架，用于使用LiDAR或RGB-D传感器进行准确的在线几何变化检测。几何SLAM的最新进展使得无需后处理即可生成密集、紧密对齐的地图成为可能，但跨整个会话比较全局地图在计算上代价高昂，并且不允许单次会话的在线变化检测。CDSD相反地将映射环境空间分解为独特的场景，在这些场景中，通过比较全局地图的密集局部子集（称为子地图）可以高效地发现变化。作为第一种基于子地图的几何变化检测方法，我们识别并解决了以下核心挑战：1）识别适合变化检测且需要最少冗余信息的场景；2）为每个场景生成密集且有代表性的子地图；3）检测具有不同视野的子地图之间的变化；4）处理检测到的变化以进行实时地图重建。结果展示了我们在马里兰州格雷斯夸特斯陆军研究实验室设施收集的自定义数据集以及开源多会话变化检测数据集上的算法性能。
英文摘要
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.
