Abstract
Abstract. Up-to-date and reliable 3D information of indoor environments is a prerequisite for many location- based services. One possibility to capture the necessary 3D data is to make use of Mobile Mapping Systems (MMSs) which rely for instance on SLAM (simultaneous localization and mapping). In most indoor environments, MMSs are by far faster than classic static systems. Moreover, they might deliver the point clouds with higher degree of completeness. In this paper, the geometric quality of point clouds of a state-of-the-art MMS (Viametris iMS3D) is investigated. In order to quantify the quality of iMS3D MMS, four different evaluation strategies namely cloud to cloud, point to plane, target to target and model based evaluation are employed. We conclude that the measurement accuracies are better than 1 cm and the precision of the point clouds are better than 3 cm in our experiments. For indoor mapping applications with few centimeters accuracy, the system offers a very fast solution. Moreover, as a nature of the current SLAM-based approaches, trajectory loop should be closed, but in some practical situations, closing the local trajectory loop might not be always possible. Our observation reveals that performing continuous repeated scanning could decrease the destructive effect of local unclosed loops.
Highlights
Up-to-date and reliable 3D information of indoor environments is a prerequisite for many location- based services and applications such as Building Information Modeling (BIM), facility management, cultural heritage documentation, and post-hazard rescue management
The most promising approaches for these conditions are based on Mobile Mapping Systems which mainly rely on SLAM algorithms and/or high-grade IMUs like the technology which is employed in Trimble indoor mobile mapping solutions (TIMMS)
2.5.1 Cloud to cloud distance: Considering the P20 data as the reference, all iMS3D Point clouds are registered to the terrestrial laser scanners (TLS) point cloud using a six parameter similarity transform (3D- Helmert transformation with fixed scale), followed by iterative closest point (ICP) algorithm which is utilized for fine registration of the point clouds
Summary
Up-to-date and reliable 3D information of indoor environments is a prerequisite for many location- based services and applications such as Building Information Modeling (BIM), facility management, cultural heritage documentation, and post-hazard rescue management. It is quite useful for machine tracking and materials transportation inside factories Classic approaches such as using total stations and terrestrial laser scanners (TLS) might not be efficient for large and rapidly changing indoor environments. Most of these systems are by far faster than classic devices Thanks to their mobile nature, the completeness of the generated point cloud can be much higher than the station-based static systems, which requires high-skilled operators to co-register the stations, accurately. These benefits are especially handy in complex and daily changing indoor environments. One aim is to analyze the effect of this real life situation on the (local) quality of the generated point cloud
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