SEAM：轨迹变形下基于子图锚定证据的终身LiDAR建图
SEAM: Submap-Anchored Evidence for Lifelong LiDAR Mapping under Trajectory Deformation
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中文总结 AI 辅助
SEAM提出基于子图锚定证据的终身LiDAR建图框架，通过方向性证据模型和DOP置信度抑制不可靠回环，实现高精度快速建图。
中文摘要 AI 辅助
我们提出了SEAM，一个基于LiDAR的终身建图框架。与依赖跨越整个会话的单一锚点不同，SEAM基于子图级锚点优化的轨迹生成证据，并执行动态物体移除和变化检测。通过子图级重投影，即使轨迹随后被新会话修改，生成的证据仍然可用，从而无需从头重新计算整个过程。SEAM使用基于DOP的置信度度量来抑制几何上不可靠的会话间回环边。抑制不可靠的回环边可防止对齐误差。SEAM还使用方向性体素级证据模型。该模型考虑了随射线方向变化的占据模式。方向感知的证据更精确地区分动态物体与环境变化。在真实建筑工地数据集和长期多会话数据集上的实验表明，SEAM比现有方法实现了更高的精度和更快的处理速度。
英文摘要
We propose SEAM, a LiDAR-based lifelong mapping framework. Instead of relying on a single anchor spanning the entire session, SEAM generates evidence based on a trajectory optimized with submap-level anchors, and performs dynamic object removal and change detection. Through submap-level reprojection, the generated evidence remains usable even if the trajectory is subsequently modified by a new session, eliminating the need to recompute the entire process from scratch. SEAM suppresses geometrically unreliable inter-session loop edges using a DOP-based confidence measure. Suppressing unreliable loop edges prevents alignment errors. SEAM also uses a directional voxel-wise evidence model. The model accounts for occupancy patterns that vary with ray direction. Direction-aware evidence separates dynamic objects from environmental changes more precisely. Experiments on a real construction-site dataset and a long-term multi-session dataset show that SEAM achieves higher accuracy and faster processing than existing methods.
