Outdoor Mapping Framework: from Images to 3D Model

Skuratovskyi, Sergiy; Gorovyi, Ievgen M.; Vovk, Vitalii; Sharapov, Dmytro S.

doi:10.1109/sps.2019.8882019

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Traditional manual ISE is tedious and requires excessive manual intervention. Compared with the structure extraction of the outdoor environment [9,10], ISE is more challenging in terms of three aspects: it requires (a) a higher quantity of clutter, (b) a more complex geometry, and (c) a more uneven-distributed occlusions.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%