用单一框架同时处理动态物体清除与变化检测，提升长期激光地图维护效率。
MTD-Map: Single-Stage Long-Term LiDAR Map Maintenance Framework via Mixture Transition Distribution
- 基于混合转移分布建模占用状态变化方向与持续时间。
- 递归形式捕捉高阶时序依赖，有效识别静态结构变化。
- 自适应策略平衡噪声抑制与准静态结构保留，适合自动驾驶长期建图。
尽管鲁棒的地图维护已取得显著进展，但现有研究多聚焦于特定任务，如动态物体移除或变化检测。本文从整体视角出发，提出 MTD-Map——一种无需独立模块的单阶段框架，可同时完成动态物体清除与变化检测。该方法采用显式表示，通过混合转移分布（MTD）建模占用状态转移的方向与持续时间。我们设计了递归的MTD形式，将历史占用模式编码为增强状态，以捕捉高阶时序依赖。此外，提出一种稳定性驱动的自适应策略，在抑制噪声的同时保留准静态结构。大量实验表明，MTD-Map能有效移除动态物体，实现具有竞争力的变化检测性能，并显著降低计算开销。
原文摘要 · Abstract (English)
While robust map maintenance has advanced significantly, existing studies have focused on specific tasks, especially dynamic object removal or change detection. In this paper, we take a holistic view of the map maintenance problem and propose MTD-Map, a single-stage framework that handles both dynamic object removal and change detection without separate task-specific modules. MTD-Map employs an explicit representation that compactly encodes the direction and duration of occupancy transitions through Mixture Transition Distribution (MTD) modeling. We develop a recursive MTD formulation that encodes historical occupancy patterns into an augmented state to capture high-order temporal dependencies. Furthermore, a stability-driven adaptive strategy balances noise suppression with the preservation of quasi-static structures. Extensive experiments verify that MTD-Map robustly removes dynamic objects and achieves competitive change detection performance, subsequently reducing computational costs. Our project page is available at: https://taeyoung96.github.io/mtd_map/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。