Indoor scene segmentation based on 3-D laser point cloud is important for rebuilding and classification, especially for permanent building structure. However, the existing segmentation methods mainly focus on the large-scale planar structures but ignore the other sharp structures and details, which would cause accuracy degradation in scene reconstruction. To handle this issue, an iterative Gaussian mapping-based segmentation strategy has been proposed in this article, which goes from rough segmentation to refined one iteratively to decompose the indoor scene into detectable point cloud clusters layer by layer. An improved model fitting algorithm based on the maximum likelihood estimation sampling consensus (MLESAC) algorithm is proposed for refined segmentation, which is called the Prior-MLESAC algorithm, to deal with the extraction of both vertical and nonvertical planar and cylindrical structures. The experimental results demonstrate that planar and cylindrical structures are segmented more completely by the proposed strategy, and more details of the indoor structure are restored than other existing methods.

The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm weights the reference points' coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of the Wi-Fi RSSI. Therefore, in order to improve the positioning accuracy of traditional location algorithms, this paper proposes a new weighted algorithm based on the physical distance of the RSSI. Experiments were conducted in an office building and the results demonstrate that the proposed method considerably outperforms the KNN, Euclidian-W-KNN, Manhattan-W-KNN, EWKNN, LiFS, and GPR in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.

This paper first proposes a new clustering algorithm for selection of reference points (RPs) based on virtual positions of APs for indoor localization in area without linear constraints, which can not only cluster automatically, but also guarantee the consistency of methods between the off-line phase clustering and the online phase positioning. A new weighted algorithm based on physical distance is then presented for position determination. With angle velocity measurement provided such as by gyroscope, the weighted algorithm is particularly suited for scenarios where the mobile moves along a trajectory. The number of clusters in traditional RP clustering algorithms needs to be predefined, which means an unsuitable number of clusters would lead to poor estimation accuracy. Traditional WKNN algorithm weights the reference points’ coordinates by the inverse of the RSSI difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of Wi-Fi RSSI. Experimental results show that the proposed weighted algorithm considerably outperforms the KNN, Euclidean-WKNN, Manhattan-WKNN, EWKNN, LiFS and GPR in terms of positioning accuracy which is defined as the cumulative distribution function of position error. The results also demonstrate that reference points in indoor area without linear constraints can be clustered automatically by the proposed clustering algorithm, and CDF of the proposed clustering algorithm outperforms KNN, WKNN, RPLC and SDC.

Point cloud registration, as the first step for the use of point cloud data, has attracted increasing attention. In order to obtain the entire point cloud of a scene, the registration of point clouds from multiple views is necessary. In this paper, we propose an automatic method for the coarse registration of point clouds. The 2D lines are first extracted from the two point clouds being matched. Then, the line correspondences are established and the 2D transformation is calculated. Finally, a method is developed to calculate the displacement along the z-axis. With the 2D transformation and displacement, the 3D transformation can be easily achieved. Thus, the two point clouds are aligned together. The experimental results well demonstrate that our method can obtain high-precision registration results and is computationally very efficient. In the experimental results obtained by our method, the biggest rotation error is 0.5219^o, and the biggest horizontal and vertical errors are 0.2319 m and 0.0119 m, respectively. The largest total computation time is only 713.4647 s.

As one of the most common features, 3D line segments provide visual information in scene surfaces and play an important role in many applications. However, due to the huge, unstructured, and non-uniform characteristics of building point clouds, 3D line segment extraction is a complicated task. This paper presents a novel method for extraction of 3D line segment features from an unorganized building point cloud. Given the input point cloud, three steps were performed to extract 3D line segment features. Firstly, we performed data pre-processing, including subsampling, filtering and projection. Secondly, a projection-based method was proposed to divide the input point cloud into vertical and horizontal planes. Finally, for each 3D plane, all points belonging to it were projected onto the fitting plane, and the α-shape algorithm was exploited to extract the boundary points of each plane. The 3D line segment structures were extracted from the boundary points, followed by a 3D line segment merging procedure. Corresponding experiments demonstrate that the proposed method works well in both high-quality TLS and low-quality RGB-D point clouds. Moreover, the robustness in the presence of a high degree of noise is also demonstrated. A comparison with state-of-the-art techniques demonstrates that our method is considerably faster and scales significantly better than previous ones. To further verify the effectiveness of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs the extracted 2D-projected line segments for coarse registration of building point clouds.