In order to achieve accurate and robust indoor position estimation in a wireless sensor network using particle filtering (PF)-based positioning algorithms, particle impoverishment, a problem that aroused from the traditional replicating-and-replacing resampling operation, must be well addressed. The loss of particle diversity not only degrades positioning accuracy but can also result in filtering divergence. To address this problem, this article proposes an adaptive neighbor-guided particle optimization strategy to substitute the traditional resampling operation. The strategy optimizes the distribution of the posterior particles through three steps: neighbor radius calculation, true neighbor identification, and neighbor-guided attraction. The proposed strategy is then integrated into the PF framework to form a novel positioning algorithm referred to as adaptive neighbor-guided particle optimization-based PF algorithm (ANPOPF). The test results show that the integration of the proposed particle optimization strategy considerably enhances the robustness of the PF algorithm, mitigating the effects of particle impoverishment. With the aid of the strategy, the ANPOPF algorithm achieves higher positioning accuracy compared to several existing positioning algorithms. Moreover, the ANPOPF algorithm owns an affordable computation load for most real-time applications.

Building Information Modeling (BIM) has recently been widely applied in the Architecture, Engineering, and Construction Industry (AEC). BIM graphical information can provide a more intuitive display of the building and its contents. However, during the Operation and Maintenance (O&M) stage of the building lifecycle, changes may occur in the building’s contents and cause inaccuracies in the BIM model, which could lead to inappropriate decisions. This study aims to address this issue by proposing a novel approach to creating 3D point clouds for updating as-built BIM models. The proposed approach is based on Pedestrian Dead Reckoning (PDR) for an Inertial Measurement Unit (IMU) integrated with a Mobile Laser Scanner (MLS) to create room-based 3D point clouds. Unlike conventional methods previously undertaken where a Terrestrial Laser Scanner (TLS) is used, the proposed approach utilizes low-cost MLS in combination with IMU to replace the TLS for indoor scanning. The approach eliminates the process of selecting scanning points and leveling of the TLS, enabling a more efficient and cost-effective creation of the point clouds. Scanning of three buildings with varying sizes and shapes was conducted. The results indicated that the proposed approach created room-based 3D point clouds with centimeter-level accuracy; it also proved to be more efficient than the TLS in updating the BIM models.