发表机构
University of Luxembourg（卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对室内施工场景视觉SLAM漂移问题，提出MVP-SLAM，利用双鱼眼相机在线匹配墙壁与楼层平面图纠正漂移，在Hilti-Trimble挑战赛中定位与SLAM任务均获在线队伍最高排名。
AI 中文摘要
室内建筑施工场地对视觉SLAM而言是极具挑战性的环境，其中变化的照明和重复、低纹理的结构会导致系统在长轨迹上漂移，尽管在此类条件下墙壁等结构元素仍然可区分。这些建筑根据其设计阶段可获得的按计划楼层平面图进行建造，尽管实际竣工现场可能与设计存在差异，但楼层平面图仍提供度量参考，既可用于在建筑中定位系统，也可用于纠正漂移。现有方法通常使用楼层平面图离线纠正已构建的轨迹，而那些在线纠正的方法通常依赖深度传感器。我们转而提出MVP-SLAM，一种运行于两个相向鱼眼相机上的在线视觉惯性SLAM，通过一种漂移感知策略，将地图中检测到的墙壁与楼层平面图匹配，仅凭相机即可纠正漂移。随后，多阶段集成将每个匹配对逐步转化为持久纠正，使轨迹在构建过程中保持纠正并定位在楼层平面图内。MVP-SLAM在Hilti-Trimble SLAM挑战赛2026的多楼层施工现场上得到验证，在定位任务中排名22支队伍中的第2名（平均RMSE为0.29米），在SLAM任务中排名62支队伍中的第5名（0.24米），是在两项任务中在线运行、集成楼层平面图并在此内定位的队伍中排名最高的。
英文摘要
Indoor building construction sites are demanding environments for visual SLAM, where variable lighting and repetitive, low-textured structures make the system drift over long trajectories, though structural elements such as walls remain distinguishable despite these conditions. These buildings are constructed according to their as-planned floor plans, available from the design phase, and although the actual as-built site can differ from this design, floor plans still provide a metric reference, both to localize the system in the building and to correct drift. Existing methods often use the floor plan to correct an already-built trajectory offline, and those that instead correct it online typically rely on depth sensors. We instead present MVP-SLAM, an online visual-inertial SLAM on two opposite-facing fisheye cameras that corrects drift from cameras alone by matching walls detected in its map to the floor plan, through a drift-aware policy. A multi-stage integration then turns each matched pair incrementally into a persistent correction, so the trajectory stays corrected and localized within the floor plan as it is built. MVP-SLAM was validated on the multi-floor construction sites of the Hilti-Trimble SLAM Challenge 2026, ranking 2nd of 22 teams in the Localization task (0.29 m mean RMSE) and 5th of 62 teams in the SLAM task (0.24 m), the top-ranked one in both tasks among those that operate online, integrate the floor plan, and localize within it.
Comments9 pages, 4 figures. Submitted to IEEE Robotics and Automation Letters (RA-L)
