Semantic decomposition and recognition of indoor spaces with structural constraints for 3D indoor modelling

Yang, Fan; Li, Lin; Su, Fei; Li, Dalin; Zhu, Haihong; Ying, Shen; Zuo, Xiaoqing; Tang, Lei

doi:10.1016/j.autcon.2019.102913

Cited by 17 publications

(13 citation statements)

References 28 publications

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“…Some works propose the analysis and projection of 3D data onto the X-Y plane to create an occupancy grid map [ 22 , 23 ]. In [ 24 , 25 , 26 ], only vertical planes corresponding to walls are detected and projected. Treating with big quantities of 3D data cause these methods to be computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots

Gonzalez

Mora

Garrido

et al. 2022

Sensors

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Nowadays, most mobile robot applications use two-dimensional LiDAR for indoor mapping, navigation, and low-level scene segmentation. However, single data type maps are not enough in a six degree of freedom world. Multi-LiDAR sensor fusion increments the capability of robots to map on different levels the surrounding environment. It exploits the benefits of several data types, counteracting the cons of each of the sensors. This research introduces several techniques to achieve mapping and navigation through indoor environments. First, a scan matching algorithm based on ICP with distance threshold association counter is used as a multi-objective-like fitness function. Then, with Harmony Search, results are optimized without any previous initial guess or odometry. A global map is then built during SLAM, reducing the accumulated error and demonstrating better results than solo odometry LiDAR matching. As a novelty, both algorithms are implemented in 2D and 3D mapping, overlapping the resulting maps to fuse geometrical information at different heights. Finally, a room segmentation procedure is proposed by analyzing this information, avoiding occlusions that appear in 2D maps, and proving the benefits by implementing a door recognition system. Experiments are conducted in both simulated and real scenarios, proving the performance of the proposed algorithms.

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Section: Related Workmentioning

confidence: 99%

Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots

Gonzalez

Mora

Garrido

et al. 2022

Sensors

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“…Kwadjo et al [39] proposed a novel 2D matrix template representation of walls that eased operations, such as the room layout and opening detection in polynomial time. Yang et al [40] defined three types of structural constraints (semantic, geometric, and topological constraints) of architectural components in scene representation. Shi et al [41] used a minimum graph cut algorithm in room clusters to detect wall-surface objects.…”

Section: (B) Ssmmentioning

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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“…High-density laser-based 3D point clouds captured by stationary terrestrial laser scanners (TLS) [1] or indoor mobile laser scanners (IMLS) [2,3] can provide detailed architectural and geometric information. Laser point clouds are usually unstructured and lack semantic information, while automatic 3D indoor modeling is difficult [4]. Traditional methods for building a detail-rich building information model (BIM) require some basic techniques, including (1) geometric building object modeling, (2) semantic modeling, and (3) topological relationship modeling.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have been devoted to the development of automated indoor modeling methods [4][5][6][7][8] in the fields of architecture, engineering, and construction (AEC). As a necessary initial step, room segmentation can provide semantic room information as the basic unit of indoor space, which is the premise of further indoor scene understanding, object recognition, and urban computing [9,10].…”

Section: Introductionmentioning

confidence: 99%

Volumetric Representation and Sphere Packing of Indoor Space for Three-Dimensional Room Segmentation

Yang

Che

Zuo

et al. 2021

IJGI

Self Cite

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Room segmentation is a basic task for the semantic enrichment of point clouds. Recent studies have mainly projected single-floor point clouds to binary images to realize two-dimensional room segmentation. However, these methods have difficulty solving semantic segmentation problems in complex 3D indoor environments, including cross-floor spaces and rooms inside rooms; this is the bottleneck of indoor 3D modeling for non-Manhattan worlds. To make full use of the abundant geometric and spatial structure information in 3D space, a novel 3D room segmentation method that realizes room segmentation directly in 3D space is proposed in this study. The method utilizes volumetric representation based on a VDB data structure and packs an indoor space with a set of compact spheres to form rooms as separated connected components. Experimental results on different types of indoor point cloud datasets demonstrate the efficiency of the proposed method.

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Semantic decomposition and recognition of indoor spaces with structural constraints for 3D indoor modelling

Cited by 17 publications

References 28 publications

Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots

Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Volumetric Representation and Sphere Packing of Indoor Space for Three-Dimensional Room Segmentation

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