A Hybrid System Integrating SLAM and TLS for Efficient Spatial Data Acquisition

Abstract

This paper presents a hybrid TLS (Terrestrial Laser Scanning)—SLAM (Simultaneous Localization and Mapping) framework for spatial data acquisition that integrates mobile laser scanning (Hovermap) with high-precision terrestrial laser scanning (RIEGL TLS). The proposed approach combines the efficiency of SLAM-based data capture with the geometric reliability of TLS reference measurements, enabling fast and accurate generation of spatial datasets that support decision-making in architecture, urban planning, facility management, and digital transformation workflows. A key contribution of the study is a structured workflow for acquisition and processing, including TLS reference loop design, segmented SLAM data acquisition, and a dual-level validation strategy. The framework additionally incorporates partial measurement redundancy, enabling internal self-verification of data consistency and improving robustness against potential measurement errors and SLAM drift. The proposed workflow was validated in a continuously operating public building, demonstrating that high-quality spatial data can be acquired without disrupting building functionality. The resulting datasets support the generation of point clouds and CAD (Computer-Aided Design)/BIM (Building Information Modeling)-ready documentation, confirming the practical applicability of the proposed approach in reality-capture workflows and decision-support processes, including BIM, digital twin development, and other design-oriented applications.