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/.

翻译：在线变化检测对于移动机器人在动态环境中高效导航至关重要。在瞬态环境（如活跃的建筑工地或频繁重新配置的室内空间）中检测变化尤其具有挑战性，这归因于频繁的遮挡和时空变化。现有方法通常难以检测变化，并且无法在不同观测间更新地图。为解决这些局限性，我们提出了一种专为在线变化检测和长期地图维护设计的双头网络。此任务的一个关键难点在于真实世界数据的收集与对齐，因为手动标注随时间推移的结构差异既劳动密集又往往不切实际。为克服此问题，我们开发了一种数据增强策略，通过从不同场景导入元素来合成结构变化，从而在无需大量真实标注的情况下实现有效的模型训练。在真实建筑工地和室内办公环境中进行的实验表明，我们的方法能够很好地泛化到多样场景，实现高效且准确的地图更新。\resubmit{我们的源代码及补充材料可在以下网址获取：https://chamelion-pages.github.io/。}