发表机构
FloMobility Pvt. Ltd.（FloMobility 私人有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对建筑AMR导航，提出基于失效模式感知的LiDAR几何与单目语义融合方法，在Jetson AGX Orin上实时生成2D可通行性地图，并发布多模态数据集，有效纠正互补几何失效模式。
AI 中文摘要
在活跃建筑工地上，自主移动机器人（AMR）面临严峻的导航挑战：基于几何的可通行性映射（如LiDAR）会遗漏湿泥和积水混凝土等视觉上危险但几何平坦的表面，而可行驶减速带和坡道上的突变几何则会产生幻影障碍。我们提出了一种在NVIDIA Jetson AGX Orin上运行的实时、失效模式感知的多模态可通行性处理流程，其中LiDAR作为主要几何安全估计，单目语义作为选择性、类别和置信度门控的修正信号。该表示保留不同的可通行类别，即平坦道路、地形和岩石地形，同时标记建筑危险。我们还发布了一个来自定制AMR的多模态建筑工地数据集：来自两个活跃工地的四个闭环ROS 2序列（RGB、深度、LiDAR、IMU、GPS-RTK、里程计），以及跨28个语义类别的506个标注帧。通过将LiDAR投影到密集语义掩码上，利用形态学修复解决稀疏性，并应用与Patchwork++的失效模式感知融合，该系统为局部AMR代价地图纠正互补的几何失效模式。
英文摘要
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.
CommentsExtended version of a paper presented at the 5th Workshop on Future of Construction, IROS 2026
