外观
论文arXiv 2610.0550524 天前
面向施工AMR的鲁棒2D可通行性建图：通过故障模式感知融合LiDAR几何与单目语义
原文链接：Robust 2D Traversability Mapping for Construction AMRs via Failure-Mode-Aware Fusion of LiDAR Geometry and Monocular Semantics（arXiv 2610.05505 · 发布日期：2026-10-04）
Robust 2D Traversability Mapping for Construction AMRs via Failure-Mode-Aware Fusion of LiDAR Geometry and Monocular Semantics
针对施工AMR的可通行性误判，提出LiDAR几何与单目语义的实时故障模式感知融合，并在自定义AMR施工场地数据上验证。
核心要点
- 施工现场中，LiDAR会漏检平坦视觉危险，也会把可行驶突变几何误判为障碍。
- 方法以LiDAR为主要几何安全估计，用单目语义作按类别和置信度门控的选择性修正。
- 系统在Jetson AGX Orin上实时运行，结合语义掩码投影、形态学补全与Patchwork++融合。
- 发布自定义AMR多模态施工场地数据，含两处现场四段ROS 2序列和506帧28类标注。
验证范围与局限：验证范围为自定义AMR在两个活跃施工场地采集的四段闭环ROS 2序列。
英文摘要 (Abstract)
Autonomous Mobile Robots (AMRs) on active construction sites face severe navigational challenges: geometry-based traversability mapping (e.g., LiDAR) misses visually hazardous but geometrically flat surfaces like wet mud and ponding concrete, while abrupt geometry on drivable speed-breakers and inclines produces phantom obstacles. We propose a real-time, failure-mode-aware multimodal traversability pipeline on an NVIDIA Jetson AGX Orin, where LiDAR is the primary geometric safety estimate and monocular semantics act as a selective, class- and confidence-gated corrective signal. The representation retains distinct traversable classes, namely flat road, terrain, and rocky terrain, while flagging construction hazards. We also release a multimodal construction-site dataset from a custom AMR: four closed-loop ROS 2 sequences from two active sites (RGB, depth, LiDAR, IMU, GPS-RTK, odometry) plus 506 annotated frames across 28 semantic classes. By projecting LiDAR onto dense semantic masks, resolving sparsity via morphological in-painting, and applying failure-mode-aware fusion with Patchwork++, the system corrects complementary geometric failure modes for a local AMR costmap.
作者：Manoj Karnekar, Om Mandhane, Gautham Ramkumar
来源
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