Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System.

The latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registration, which aligns raw point cloud data from separate local coordinate systems into a unified coordinate system, still relies on manual intervention. To address this practical issue, this study presents an automated point cloud registration approach optimized for a stop-and-go scanning system based on a quadruped walking robot. The proposed approach comprises three main phases: perpendicular constrained wall-plane extraction; coarse registration with plane matching using point-to-point displacement calculation; and fine registration with horizontality constrained iterative closest point (ICP). Experimental results indicate that the proposed method successfully achieved automated registration with an accuracy of 0.044 m and a successful scan rate (SSR) of 100% within a time frame of 424.2 s with 18 sets of scan data acquired from the stop-and-go scanning system in a real-world indoor environment. Furthermore, it surpasses conventional approaches, ensuring reliable registration for point cloud pairs with low overlap in specific indoor environmental conditions.

Analysis of accessibility to emergency rooms by dynamic population from mobile phone data: Geography of social inequity in South Korea.

Accessibility of emergency medical care is one of the crucial factors in evaluating national primary medical care systems. While many studies have focused on this issue, there was a fundamental limit to the measurement of accessibility of emergency rooms, because the commonly used census-based population data are difficult to provide realistic information in terms of time and space. In this study, we evaluated the geographical accessibility of emergency rooms in South Korea by using dynamic population counts from mobile phone data. Such population counts were more accurate and up-to-date because they are obtained by aggregating the number of mobile phone users in a 50-by-50 m grid of a locational field, weighted by stay time. Considering both supply and demand of emergency rooms, the 2-step floating catchment analysis was implemented. As a result, urban areas, including the capital city Seoul, showed lower accessibility to emergency rooms, whereas rural areas recorded higher accessibility. This result was contrary to the results analyzed by us based on census-based population data: higher accessibility in urban areas and lower in rural. This implies that using solely census data for accessibility analysis could lead to certain errors, and adopting mobile-based population data would represent the real-world situations for solving problems of social inequity in primary medical care.

Development of kinematic 3D laser scanning system for indoor mapping and as-built BIM using constrained SLAM.

The growing interest and use of indoor mapping is driving a demand for improved data-acquisition facility, efficiency and productivity in the era of the Building Information Model (BIM). The conventional static laser scanning method suffers from some limitations on its operability in complex indoor environments, due to the presence of occlusions. Full scanning of indoor spaces without loss of information requires that surveyors change the scanner position many times, which incurs extra work for registration of each scanned point cloud. Alternatively, a kinematic 3D laser scanning system, proposed herein, uses line-feature-based Simultaneous Localization and Mapping (SLAM) technique for continuous mapping. Moreover, to reduce the uncertainty of line-feature extraction, we incorporated constrained adjustment based on an assumption made with respect to typical indoor environments: that the main structures are formed of parallel or orthogonal line features. The superiority of the proposed constrained adjustment is its reduction for uncertainties of the adjusted lines, leading to successful data association process. In the present study, kinematic scanning with and without constrained adjustment were comparatively evaluated in two test sites, and the results confirmed the effectiveness of the proposed system. The accuracy of the 3D mapping result was additionally evaluated by comparison with the reference points acquired by a total station: the Euclidean average distance error was 0.034 m for the seminar room and 0.043 m for the corridor, which satisfied the error tolerance for point cloud acquisition (0.051 m) according to the guidelines of the General Services Administration for BIM accuracy.