Owing to complexity of indoor environment, such as close range, multi-angle, occlusion, uneven lighting conditions and lack of absolute positioning information, quality assessment of indoor mobile mapping point clouds is a tough and challenging task. It is meaningful to evaluate the features extracted from indoor point clouds prior to further quality assessment. In this paper, we mainly focus on feature extraction depend upon indoor RGB-D camera for the quality assessment of point cloud data, which is proposed for selecting and screening local features, using random forest algorithm to find the optimum feature for the next step’s quality assessment. First, we collect indoor point clouds data and classify them into classes of complete or incomplete. Then, we extract high dimensional features from the input point clouds data. Afterwards, we select discriminative features through random forest. Experimental results on different classes demonstrate the effective and promising performance of the presented method for point clouds quality assessment.
In this paper, a new method of unsupervised change detection is proposed by modeling multi-scale change detector based on local mixed information and we present a method of automated threshold. A theoretical analysis is presented to demonstrate that more comprehensive information is taken into account by the integration of multi-scale information. The ROC curves show that change detector based on multi-scale mixed information(MSM) is more effective than based on mixed information(MIX). Experiments on artificial and real-world datasets indicate that the multi-scale change detection of mixed information can eliminate the pseudo-change part of the area. Therefore, the proposed algorithm MSM is an effective method for the application of change detection.
In order to perform high precise calibration of camera in complex background, a novel design of planar composite target and the corresponding automatic extraction algorithm are presented. Unlike other commonly used target designs, the proposed target contains the information of feature point coordinate and feature point serial number simultaneously. Then based on the original target, templates are prepared by three geometric transformations and used as the input of template matching based on shape context. Finally, parity check and region growing methods are used to extract the target as final result. The experimental results show that the proposed method for automatic extraction and recognition of the proposed target is effective, accurate and reliable.
This paper presents an automatic algorithm to localize and extract urban trees from mobile LiDAR point clouds. First, in order to reduce the number of points to be processed, the ground points are filtered out from the raw point clouds, and the un-ground points are segmented into supervoxels. Then, a novel localization method is proposed to locate the urban trees accurately. Next, a segmentation method by localization is proposed to achieve objects. Finally, the features of objects are extracted, and the feature vectors are classified by random forests trained on manually labeled objects. The proposed method has been tested on a point cloud dataset. The results prove that our algorithm efficiently extracts the urban trees.
The information of individual trees plays an important role in urban surveying and mapping. With the development of Light Detection and Ranging (LiDAR) technology, 3-Dimenisonal (3D) structure of trees can be generated in point clouds with high spatial resolution and accuracy. Individual tree segmentations are used to derive tree structural attributes such as tree height, crown diameter, stem position etc. In this study, a framework is proposed to take advantage of the detailed structures of tree crowns which are represented in the mobile laser scanning (MLS) data. This framework consists of five steps: (1) Automatically detect and remove ground points using RANSAC; (2) Compress all the above ground points to image grid with 3D knowledge reserved; (3) Simplify and remove unqualified grids; (4) Find tree peaks using a heuristic searching method; (5) Delineate the individual tree crowns by applying a modified watershed method. In an experiment on the point clouds on Xiamen Island, China, individual tree crowns from MLS point cloud data are successfully extracted.
Modern urban life is becoming increasingly more dependent on reliable electric power supply. Since power outages cause substantial financial losses to producers, distributors and consumers of electric power, it is in the common interest to minimize failures of power lines. In order to detect defects as early as possible and to plan efficiently the maintenance activities, distribution networks are regularly inspected. Carrying out foot patrols or climbing the structures to visually inspect transmission lines and aerial surveys (e.g., digital imaging or most recent airborne laser scanning (ALS) are the two most commonly used methods of power line inspection. Although much faster in comparison to the foot patrol inspection, aerial inspection is more expensive and usually less accurate, in complex urban areas particularly. This paper presents a scientific work that is done in the use of mobile laser scanning (MLS) point clouds for automated extraction of power lines. In the proposed method, 2D power lines are extracted using Hough transform in the projected XOY plane and the 3D power line points are visualized after the point searching. Filtering based on an elevation threshold is applied, which is combined with the vehicle’s trajectory in the horizontal section.