外观
论文arXiv 2609.188191 个月前
SEAM：轨迹变形下用于终身 LiDAR 建图的子地图锚定证据
原文链接：SEAM: Submap-Anchored Evidence for Lifelong LiDAR Mapping under Trajectory Deformation（arXiv 2609.18819 · 发布日期：2026-09-16）
SEAM: Submap-Anchored Evidence for Lifelong LiDAR Mapping under Trajectory Deformation
SEAM用子地图级锚点优化轨迹生成证据，进行动态物体移除和变化检测，并在真实施工现场与长期多会话数据集验证。
核心要点
- 单一会话全局锚点下，轨迹被新会话修改会使证据面临重算问题。
- SEAM基于子地图级锚点优化轨迹，并通过子地图级重投影复用证据。
- 它用DOP置信度抑制不可靠跨会话回环边，并采用方向体素证据模型。
- 实验显示，在两个真实数据集上，SEAM精度和处理速度高于现有方法。
验证范围与局限：验证范围为真实施工现场数据集和长期多会话数据集。
英文摘要 (Abstract)
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.
作者：Kyuwon Kim
来源
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