Chamelion：在瞬态环境中实现长期LiDAR映射的可靠变化检测
Chamelion: Reliable Change Detection for Long-Term LiDAR Mapping in Transient Environments
AI总结：
Chamelion通过双头网络实现在线变化检测和长期LiDAR地图维护，利用数据增强策略提升在瞬态环境中的地图更新效率与准确性。
摘要：
在线变化检测对于移动机器人高效导航动态环境至关重要。在瞬态设置中，如活跃的施工现场或频繁重新配置的室内空间，检测变化尤其具有挑战性，因为频繁的遮挡和时空变化使得现有方法难以检测变化并跨不同观测更新地图。为了解决这些限制，我们提出了一种双头网络，用于在线变化检测和长期地图维护。这项任务的关键困难在于现实数据的收集和对齐，因为手动注册结构差异随时间变化既耗时又往往不切实际。为此，我们开发了一种数据增强策略，通过导入不同场景的元素合成结构变化，从而在无需大量地面真实标注的情况下实现有效的模型训练。在真实施工现场和室内办公室环境中进行的实验表明，我们的方法在各种场景中表现良好，实现了高效且准确的地图更新。
英文摘要：
Online change detection is crucial for mobile robots to efficiently navigate through dynamic environments. Detecting changes in transient settings, such as active construction sites or frequently reconfigured indoor spaces, is particularly challenging due to frequent occlusions and spatiotemporal variations. Existing approaches often struggle to detect changes and fail to update the map across different observations. To address these limitations, we propose a dual-head network designed for online change detection and long-term map maintenance. A key difficulty in this task is the collection and alignment of real-world data, as manually registering structural differences over time is both labor-intensive and often impractical. To overcome this, we develop a data augmentation strategy that synthesizes structural changes by importing elements from different scenes, enabling effective model training without the need for extensive ground-truth annotations. Experiments conducted at real-world construction sites and in indoor office environments demonstrate that our approach generalizes well across diverse scenarios, achieving efficient and accurate map updates.\resubmit{Our source code and additional material are available at: https://chamelion-pages.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
