用动态性建模实现激光雷达长期地图的自动更新与去噪
Ephemerality meets LiDAR-based Lifelong Mapping
- 通过双时域动态性概率建模，区分短期临时物体与长期静态结构
- 在真实长期数据集上实现无需人工干预的地图更新与动态物体移除
- 适合需要长期自主运行的移动机器人系统，尤其动态环境中的导航
长期地图构建对机器人在动态环境中的长期部署至关重要。本文提出ELite框架，一种基于激光雷达的长效地图系统，可无缝对齐多时段数据、移除动态物体，并以端到端方式更新地图。传统地图元素通常分为静态或动态，但如停放车辆等场景表明需更细粒度分类。本方法核心是将世界概率建模为两阶段‘动态性’，表征点在不同时间尺度下的瞬时性。通过利用动态性编码的空间-时间上下文，ELite能准确识别暂态地图元素，维持可靠且实时更新的静态地图，并以更精细方式提升新数据对齐鲁棒性。在长期真实数据集上的大量实验验证了系统的鲁棒性与有效性。源代码已公开：https://github.com/dongjae0107/ELite。
原文摘要 · Abstract (English)
Lifelong mapping is crucial for the long-term deployment of robots in dynamic environments. In this paper, we present ELite, an ephemerality-aided LiDAR-based lifelong mapping framework which can seamlessly align multiple session data, remove dynamic objects, and update maps in an end-to-end fashion. Map elements are typically classified as static or dynamic, but cases like parked cars indicate the need for more detailed categories than binary. Central to our approach is the probabilistic modeling of the world into two-stage $\textit{ephemerality}$, which represent the transiency of points in the map within two different time scales. By leveraging the spatiotemporal context encoded in ephemeralities, ELite can accurately infer transient map elements, maintain a reliable up-to-date static map, and improve robustness in aligning the new data in a more fine-grained manner. Extensive real-world experiments on long-term datasets demonstrate the robustness and effectiveness of our system. The source code is publicly available for the robotics community: https://github.com/dongjae0107/ELite.
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