The increasing availability of both indoor positioning services and sensors for 3D data capture, such as RGB-D sensors, allows the provision of indoor spatial information services for indoor localization-based applications. To efficiently realize these services, the indoor information and the relationships between indoor spaces are required. The recently released Indoor Geography Markup Language (IndoorGML) attempts to represent and exchange geo-information for modeling topology and semantics of indoor spaces. However, it is still challenging to map indoor space features to the IndoorGML-encoded navigation network model directly from colorized 3D points. Therefore, we propose a semantics-guided method for indoor navigation element reconstruction from RGB-D sensor data. First, a hierarchical indoor scene interpretation framework is used for robustly recognizing the architecture structures and doors, respectively. In the developed hierarchical structure, a graph convolutional network-based architectural structure recognition method is adopted to deduce the long-range interactions among primitives for describing the rich physical relationships in the real world. Its output is the produced initial annotated results, from which doors as the common openings are further detected using a U-Net-based door recognition method. This enables to effectively provide the semantic guidance for the cellular representation of the indoor space and its topological reconstruction. Second, an adaptive architectural structure-guided room segmentation method is developed by combining distance transform and watershed segmentation to determine cellular spaces according to the definition in IndoorGML. Third, taking the different states of doors into consideration, a door-guided topological relationship reconstruction method is proposed to achieve the network graph representation of indoor environments. In this context, a simulated door model is designed to correct and update the true position of a door leaf, and a virtual door is defined to optimize the topological analysis. As a consequence, an IndoorGML-encoded navigation network model is generated, which can be used as the base for indoor navigation applications independent of the platform. Experiments are performed on the public Stanford large-scale 3D Indoor Spaces Dataset to verify the robustness and effectiveness of the proposed method both qualitatively and quantitatively. Results indicate the capability of the proposed method in automatically reconstructing indoor navigation elements of Manhattan-world indoor environments from RGB-D sensor data.

室内定位服务和用于3D数据捕获的传感器（例如RGB-D传感器）的可用性不断提高，从而可以为基于室内定位的应用程序提供室内空间信息服务。为了有效地实现这些服务，需要室内信息以及室内空间之间的关系。最近发布的室内地理标记语言（IndoorGML）尝试表示和交换地理信息，以对室内空间的拓扑和语义建模。但是，直接从彩色3D点将室内空间特征映射到IndoorGML编码的导航网络模型仍然具有挑战性。因此，我们提出了一种从RGB-D传感器数据重建室内导航元素的语义指导方法。第一，分层的室内场景解释框架分别用于稳健地识别建筑结构和门。在已开发的层次结构中，采用基于图卷积网络的体系结构识别方法来推导基元之间的远程交互作用，以描述现实世界中丰富的物理关系。它的输出是产生的初始注释结果，使用基于U-Net的门识别方法，可以进一步检测出作为公共开口的门。这使得能够有效地为室内空间的蜂窝表示及其拓扑重建提供语义指导。第二，通过将距离变换和分水岭分割相结合，根据IndoorGML中的定义，确定了一种自适应的建筑结构引导房间分割方法。第三，考虑门的不同状态，提出了一种门导向的拓扑关系重构方法，以实现室内环境的网络图表示。在这种情况下，将模拟门模型设计为校正和更新门扇的真实位置，并定义虚拟门以优化拓扑分析。结果，生成了IndoorGML编码的导航网络模型，该模型可用作独立于平台的室内导航应用程序的基础。在公开的斯坦福大学大型3D室内空间数据集上进行了实验，以定性和定量地验证了该方法的鲁棒性和有效性。结果表明，该方法具有从RGB-D传感器数据自动重构曼哈顿世界室内环境的室内导航元素的能力。