Conventional sensor-based localization relies on high-precision maps. These maps are generally built using specialized mapping techniques, which involve high labor and computational costs. While in the architectural, engineering and construction industry, building information models (BIMs) are available and can provide informative descriptions of environments. This paper explores an effective way to localize a mobile 3D LiDAR sensor in BIM considering both geometric and semantic properties. Specifically, we first convert original BIM to semantic maps using categories and locations of BIM elements. After that, a coarse-to-fine semantic localization is performed to align laser points to the map via iterative closest point registration. The experimental results show that the semantic localization can track the pose with only scan matching and present centimeter-level errors over 340 meters traveling, thus demonstrating the feasibility of the proposed mapping-free localization framework. The results also show that using semantic information can help reduce localization errors in BIM.
翻译:以常规传感器为基础的本地化取决于高精度地图。 这些地图通常使用专门的绘图技术构建,这涉及高人工和计算成本。 在建筑、工程和建筑行业,建材信息模型(BIM)可以提供环境信息描述。本文探讨了将移动的3D LiDAR传感器定位在BIM的一个有效方法,既考虑到几何特性,又考虑到语义特性。具体地说,我们首先使用BIM元素的类别和位置将原BIM转化为语义图。之后,通过迭代最接近点的注册,对激光点进行粗略到平面的语义本地化,以将激光点与地图相匹配。实验结果表明,语义本地化只能通过扫描匹配和显示超过340米旅行的厘米级误差来跟踪图像,从而证明拟议的无绘图本地化框架的可行性。结果还表明,使用语义信息可以帮助减少BIM的本地化错误。