Laser scanning technology has long been the preferred method for capturing interior scenes in various industries. With a growing market, smaller and more affordable scanners have emerged, offering end products with sufficient accuracy. While not on par with professional scanners, Apple has made laser scanning technology accessible to users with the introduction of the new iPhone Pro models, democratizing 3D scanning. Thus, this study aimed to assess the performance of the iPhone’s lidar technology as a low-cost solution for building documentation. Four scanning applications were evaluated to determine the accuracy, precision, and user experience of the generated point clouds compared with a terrestrial laser scanner. The results reveal varying performances on the same device, highlighting the influence of software. Notably, there is room for improvement, particularly in tracking the device’s position through software solutions. As it stands, the technology is well suited for applications such as indoor navigation and the generation of quick floor plans in the context of building documentation.

This paper addresses the calibration of mobile mapping systems and the feasibility of using a total station as a sensor for indoor mobile mapping systems. For this purpose, the measuring system of HafenCity University in Hamburg is presented and discussed. In the second part of the calibration, the entire system will be described regarding the interaction of laser scanners and other parts of the system. Finally, the preliminary analysis of the use of a total station is presented in conjunction with the measurement system. The difficulty of time synchronization is also discussed. In multiple tests, a comparison was made versus a reference solution based on GNSS. Additionally, the suitability of the total station was also considered for indoor applications.

While outdoor navigation systems are already represented everywhere, the enclosed space is much less developed. The project Level 5 Indoor Navigation (L5IN) presents a new approach with mobile phone standard 5G as the orientation signal and without additional infrastructure for navigation in indoor environments. The aim of this project is to use the new available 5G technology to show how navigation systems, which have thus far only been available in the outdoor segment, can now be integrated into existing smartphone systems for indoor navigation. This paper gives an overview of the project and presents the different work packages leading to a holistic approach towards the development of an indoor navigation application for pedestrians. By using a specific app with open interfaces, it is planned to make navigation possible in all buildings modeled according to certain standards. The challenge involved is that, unlike outdoor maps, there is no map basis for buildings. For this reason, different approaches to map generation were examined. In a building information model (BIM), all information will be collected and made available via a database for positioning and visualization. The focus is furthermore on positioning, achieved through smartphone sensors and 5G, so that users can orientate themselves in buildings without having to connect to singular systems. It shall be shown that positioning with a standard deviation of 2–3 m and a confidence interval of 68 % is possible. Another advantage of 5G, the ability to send real-time data in higher data packages, will be used for data transmission. The basic idea of 5G-based indoor navigation will be enabled with radio cells of the providers, which will be set up on the HafenCity University campus. The complex university building will be used as a prototype environment.

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure.

There are a number of consumer electronics applications on the market that use the Global Navigation Satellite System (GNSS). Smartphones equipped with GNSS are used for navigation. In surveying, GNSS-based mobile mapping systems (MMS) are used daily to capture the local environment in three dimensions. In recent years, research into indoor applications has increased, particularly the mapping of building interiors using kinematic measurement methods and navigation indoors. A significant problem is the loss of GNSS-based positioning information. This problem could be eliminated using an Inertial Measurement Unit and/or a combination of additional information. HafenCity University (HCU) is engaged in the development of a modular mobile mapping platform, known as a mobile mapping system (MMS), that can also be used indoors. Furthermore, HCU is working on algorithms for indoor navigation that use commercially available smartphones, thus obviating costly infrastructure. Base maps are important for visual reference and can be used to provide supporting information in navigation algorithms, but constructing base maps is often problematic. The MMS is used for creating maps for indoor navigation. In this paper, a concept of the interaction of the two research areas is presented, and insight on the states of both research areas is given.

Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms.