In this paper, we introduce an automatic room segmentation approach based on morphological processing. The inputs are registered point-clouds obtained from either a static laser scanner or a mobile scanning system, without any required prior information or initial labeling satisfying specific conditions. The proposed segmentation method’s main concept, based on the assumption that each room is bound by vertical walls, is to project the 3D point cloud onto a 2D binary map and to close all openings (e.g., doorways) to other rooms. This is achieved by creating an initial segment map, skeletonizing the surrounding walls of each segment, and iteratively connecting the closest pixels between the skeletonized walls. By iterating this procedure for all initial segments, the algorithm produces a “watertight” floor map, on which each room can be segmented by a labeling process. Finally, the original 3D points are segmented according to their 2D locations as projected on the segment map. The novel features of our approach are: (1) its robustness against occlusions and clutter in point-cloud input; (2) high segmentation performance regardless of the number of rooms or architectural complexity; and (3) straight segmentation boundary generation, all of which were proved in experiments with various sets of real-world, synthetic, and publicly available data. Additionally, comparisons with the five popular existing methods through both qualitative and quantitative evaluations demonstrated the feasibility of the proposed approach.

Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information.

Tree information such as tree height, tree type, diameter at breast height, and number of trees are critical for effective forest analysis and management. In this regard, this paper presents an individual tree extraction method that uses airborne LiDAR (Light Detection and Ranging) and hyperspectral data. The Support Vector Machine (SVM) classifier was first used to extract tree areas from hyperspectral imagery. Then, Principal Components Analysis (PCA) was applied to the hyperspectral imagery to derive a PCA image consisting of five bands. A segmentation process was performed on the four datasets, which are 1) hyperspectral image, 2) LiDAR-based DCM (Digital Canopy Model), 3) Fusion of PCA image and LiDAR-DCM, and 4) Fusion of PCA image, LiDAR-based DCM and NDVI (Normalized Difference Vegetation Index). Finally, individual tree parameters were estimated based on the segmentation results. The field data were then compared with the final results. The result shows that using fusion data set our method can detect 70% and 92% of reference trees in the forest areas with high and low density, respectively. Conclusively, the tree extraction approach based on the fusion of different data sources provided better results than ones using the single data sources.

The fish-eye lens camera offers the advantage of efficient acquisition of image data through a wide field of view. However, unlike the popular perspective projection camera, a strong distortion effect appears as the periphery of the image is compressed. Such characteristics must be precisely analyzed through camera self-calibration. In this study, we carried out a fish-eye lens camera self-calibration while considering different types of test objects and projection models. Self-calibration was performed using the V-, A-, Plane-, and Room-type test objects. In the fish-eye lens camera, the V-type test object was the most advantageous for ensuring the accuracy of the principal point coordinates and focal length, because the correlations between parameters were relatively low. On the other hand, the other test objects were advantageous for ensuring the accuracy of distortion parameters because of the well-distributed image points. Based on the above analysis, we proposed, an accurate fish-eye lens camera self-calibration method that applies the V-type test object. The RMS-residuals of the proposed method were less than 1 pixel.

The Simultaneous Localization and Mapping (SLAM) technique has been used for autonomous navigation of mobile systems; now, its applications have been extended to 3D data acquisition of indoor environments. In order to reconstruct 3D scenes of indoor space, the kinematic 3D laser scanning system, developed herein, carries three laser range finders (LRFs): one is mounted horizontally for system-position correction and the other two are mounted vertically to collect 3D point-cloud data of the surrounding environment along the system’s trajectory. However, the kinematic laser scanning results can be impaired by errors resulting from sensor misalignment. In the present study, the bore-sight calibration of multiple LRF sensors was performed using a specially designed double-deck calibration facility, which is composed of two half-circle-shaped aluminum frames. Moreover, in order to automatically achieve point-to-point correspondences between a scan point and the target center, a V-shaped target was designed as well. The bore-sight calibration parameters were estimated by a constrained least squares method, which iteratively minimizes the weighted sum of squares of residuals while constraining some highly-correlated parameters. The calibration performance was analyzed by means of a correlation matrix. After calibration, the visual inspection of mapped data and residual calculation confirmed the effectiveness of the proposed calibration approach.

Recently, an increasing interest in the Location-Based Services (LBS) has led to the needs for studies on indoor localization. Therefore, this study aims to propose an image-based indoor localization system, which can improve the practicality and efficiency for wider applications. In order to construct reference imagery database, an optical camera and a 2D laser scanner mounted on a mobile platform were utilized and the data acquired was registered to the real world coordinate system. The localization procedure was then conducted based on Bag of Words (BoW) algorithm which matches the images by evaluating the similarities of the histograms produced from them. In addition, the experiments to verify the optimal system design were carried out. Compared to manual construction, the mobile platform-based data acquisition and location registration can be carried out in an easier manner. The proposed localization method provided more than 80% of localization accuracy for the query images taken in various locations and points of view. The system proposed herein is a practical, efficient, and accurate means of constructing the reference images and determining the location of a user in the indoor environment. It was also found that the size of codebook and the number of reference images should be determined while considering the computational time and the purpose of the application. Reducing the difference of the spatial resolution between the reference and the query images also provided more accurate results.