# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
###### Abstract  
This study presents a comprehensive comparison of static and SLAM (Simultaneous Localization and Mapping) laser scanners of both new and old generation in a controlled indoor environment of a standard commercial building with long, linear corridors and recesses. The aim was to assess both global and local accuracy, as well as noise characteristics, of each scanner. Methods: A highly accurate static scanner was used to generate a reference point cloud. Five devices were evaluated: two static scanners (Leica RTC 360 and Trimble X7) and three SLAM scanners (GeoSLAM ZEB Horizon RT, Emesent Hovermap ST-X, and FARO Orbis). Accuracy analysis included systematic and random error assessment, axis-specific displacement evaluation, and profile-based local accuracy measurements. Additionally, noise was quantified before and after data smoothing. Static scanners yielded superior accuracies, with the Leica RTC 360 achieving the best performance (absolute accuracy of 1.2 mm). Among SLAM systems, the Emesent Hovermap ST-X and FARO Orbis--both newer-generation devices--demonstrated significant improvements over the older-generation GeoSLAM ZEB Horizon RT. After smoothing, the noise levels of these new-generation SLAM scanners (approx. 2.1-2.2 mm) approached those of static systems. The findings underline the ongoing technological progress in SLAM systems, with the new-generation SLAM scanners becoming increasingly viable alternatives to static scanners, especially when speed, ease of use, and reduced occlusions are prioritized. This makes them well-suited for rapid indoor mapping applications, provided that the slightly lower accuracy is acceptable for the intended use. SLAM scanners; static scanners; indoor; comparison; local accuracy; global accuracy; testing; performance 2025158053  
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purpose in the case of linear constructions [2]. Additionally, lidar-based scanners mounted on unmanned aerial vehicles (UAVs) have also found their place in surveying [3; 4]. Scanning speeds range from hundreds of thousands to millions of points per second. Laser scanning is quite sufficient for capturing the space in many applications, such as studies on earthquake-induced landscape changes [5], digital terrain model creation [6], geohazard areas mapping [7], trees and forests [8; 9], agriculture [10], vegetation structure [11], underground structures [12], emergency vehicles passage planning [13], and even the evaluation of the avalanche hazard in steep snow-covered areas [14]. Where constructions are concerned, laser scanning is mostly used for the purposes of BIM [15], evaluation of the road condition [16], creating digital twins of bridges [17] or towers [18], and the documentation of various peculiar construction shapes, such as astronomical domes [19]. For building interiors, laser scanning methods are increasingly used as the principal surveying method allowing the creation of real-state documentation. Compared to (for example) underground spaces, such as mines or caves, the digitization of the acquired data is usually simple in buildings as it typically involves predominantly planar objects (walls, ceilings, floors) with few obstructions. The practical acquisition of a digital twin of a building interior using static scanners, which are characterized by high accuracy and low noise, is limited by the need to perform a scan separately in each room. This is significantly more time-consuming than in the case of SLAM scanners, allowing data acquisition during a simple passage through the object. In static scanners, the overall accuracy consists of the accuracy of the scanner sensor and of scanner positioning, which is typically determined either as a spatial intersection from ground control points or through the Iterative Closest Point algorithm applied to the point cloud). The assessment of SLAM scanners' accuracies is usually a more complex task as, besides the accuracy of the sensor itself, establishing the trajectory of the scanner also plays a crucial role.