Acceleration of Shadowing Detection with Octree and Improved Specular Model for Indoor Propagation Using Point Cloud Data

Pang, Min; Luo, Chunbei; Mei, Xiaowei; Lin, Hai

doi:10.1109/compem.2018.8496517

Cited by 4 publications

(2 citation statements)

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“…(b) In FSM construction, the parameters were set to improve the accuracy of the extracted structure and to preserve the structural details of the FSM. Figure 18 shows some of the parameters used in detail; δ 1 and δ 2 were approximated using Equations (15) and (16), respectively. (c) In NSM construction, the parameters used were mainly for ground extraction and PAM construction.…”

Section: Parameter Settingmentioning

confidence: 99%

“…According to the data type, ISE methods are generally classified into two major categories: (1) ISE based on image data using formatted images and small data memory, subject to illumination changes, for which some studies based on image data have been performed [11][12][13], and (2) ISE based on point cloud data using an unstructured point cloud, favorable spatial topology, and a large memory footprint, for which studies have also been performed [14][15][16][17]. Furthermore, methods combining image and point clouds have been proposed [18][19][20], and video has been exploited [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Indoor Structure Extraction Based on Dense Point Cloud

Shi

Zeng

2020

IJGI

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Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.

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Section: Parameter Settingmentioning

confidence: 99%