Align to locate: Registering photogrammetric point clouds to BIM for robust indoor localization

Abstract

Indoor localization is critical for many smart applications in built environments such as service robot navigation and facility management. Building information models (BIMs) provide new streams of spatial and visual information about building interiors that can be exploited for robust indoor localization. However, previous localization methods that used BIM were unable to achieve high precision and accuracy, limiting their practical applications. To address this challenge, a new approach, “align-to-locate (A2L)”, is proposed in this study to leverage BIM as a reference to rectify and fine-tune coarse camera poses estimated by photogrammetry. The camera pose rectification is achieved using a new registration algorithm that aligns a photogrammetric point cloud with a BIM-referenced point cloud. The experiments demonstrated the effectiveness of the proposed A2L approach, which outperformed the state of the art with a localization error of 1.07 m and an orientation deviation of 3.7°. It was also found that query point clouds generated from photographs taken along the lateral or longitude directions are more conducive for registration. While increasing the number of data collection locations and images from each location can provide higher accuracy, this approach may compromise the computational speed. This study contributes to the challenging indoor localization problem by proposing the A2L approach and evaluating its applicability for more robust camera pose estimation through point-cloud-to-BIM registration. The developed A2L approach can be integrated as a post-processing module in existing vision-based localization methods to fine-tune their estimated camera poses.