A method capable of automatically reconstructing 3D building models with semantic information from the unstructured 3D point cloud of indoor scenes is presented in this paper. This method has three main steps: 3D segmentation using a new hybrid algorithm, room layout reconstruction, and wall-surface object reconstruction by using an enriched approach. Unlike existing methods, this method aims to detect, cluster, and model complex structures without having prior scanner or trajectory information. In addition, this method enables the accurate detection of wall-surface “defacements”, such as windows, doors, and virtual openings. In addition to the detection of wall-surface apertures, the detection of closed objects, such as doors, is also possible. Hence, for the first time, the whole 3D modelling process of the indoor scene from a backpack laser scanner (BLS) dataset was achieved and is recorded for the first time. This novel method was validated using both synthetic data and real data acquired by a developed BLS system for indoor scenes. Evaluating our approach on synthetic datasets achieved a precision of around 94% and a recall of around 97%, while for BLS datasets our approach achieved a precision of around 95% and a recall of around 89%. The results reveal this novel method to be robust and accurate for 3D indoor modelling.

We propose a framework for odometry, mapping and ground segmentation using a backpack LiDAR system that achieves both real-time and low-drift performance. First, we present a spatio-temporal calibration method to carefully merge scans from the two laser scanners on a backpack. Second, we propose a feature extraction method which generalizes a point's geometrical characteristics as two groups (disjoint, continuous) and three types (edge, corner, plane). The extracted features are used in point cloud registration in the odometry and mapping tasks. Third, a fast ground segmentation method is customized for the backpack LiDAR system. Finally, we evaluate the proposed method in four datasets logged by the backpack across different scales and environments. Furthermore, NTU VIRAL dataset is used to benchmark our method quantitatively. Experiments show that our method consistently outperforms the state-of-the-art methods before using loop closure optimization and sensor fusion techniques.

In this paper, a new personal safety forewarning approach (PSFA) using GPS positioning and spatial analysis is presented. Specifically, we discussed the spatial topological relationships between the spatiotemporal objects related to personal safety. On that base, safety state is assessed through the calculation of a state matrix and then how to affirm the factors related to forewarning response is explored. Experimental results of buffer analysis show that sufficient accuracy (more than 95%) can be obtained when the target is 20 meters or farther away from the border of the test regions (even with poor GPS reception). A compromise is also adopted for the detected trajectory points near the border to decrease the error rate. Thus the practicability of the proposed method is validated.