Three-dimensional point cloud plane segmentation in both structured and unstructured environments

Xiao, Jing; Zhang, Jianhua; Adler, Benjamin; Zhang, Houxiang; Zhang, Jianwei

doi:10.1016/j.robot.2013.07.001

Cited by 91 publications

(58 citation statements)

References 33 publications

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“…Second, NDT cells from the same surface have similar plane parameters. These assumptions are similar to those in [12,31].…”

Section: Ndt-ransac Plane Segmentationmentioning

confidence: 65%

“…Xiao et al [12] took structured and unstructured environments into consideration in their work. However, we make no such distinction; instead, we uniformly consider an unstructured environment.…”

Section: Ndt Cell Sizementioning

confidence: 99%

“…The huge point-cloud data available from indoor environments have inspired deep understanding of the data on the semantic level to meet the demands of indoor mapping and reconstruction. Objects with planar surfaces such as ceilings, walls and floors abound in indoor environments, and can semantically provide a compressive representation of the point clouds; typically, the data-compression rate can reach over 90% [11,12]. The point clouds of these types of objects have a planar geometric appearance, and the plane can be extracted.…”

Section: Introductionmentioning

confidence: 99%

“…Xiao et al [12] proposed two complementary strategies, a named sub-window-based region-growing algorithm for structured environments and a hybrid region-growing algorithm for unstructured environments. A sub-window is a moving window suitable for organized point-cloud representation.…”

Section: Introductionmentioning

confidence: 99%

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An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

et al. 2017

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Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.

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“…Second, NDT cells from the same surface have similar plane parameters. These assumptions are similar to those in [12,31].…”

Section: Ndt-ransac Plane Segmentationmentioning

confidence: 65%

Section: Ndt Cell Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

et al. 2017

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“…This allows a robot without prior knowledge of its environment to isolate objects from their surroundings by moving them and observing the visual feedback. It is also worthwhile noting point cloud feature extraction algorithms which are currently being used for the perception to filter outliers from noisy data, stitch 3D point clouds together, segment relevant parts of a scene, extract key points and compute descriptors to recognize objects in the world based on their geometric appearance, and create surfaces from point clouds (e.g., [79] and [80]). …”

Section: Feature Detection and Segmentationmentioning

confidence: 99%

A Taxonomy of Vision Systems for Ground Mobile Robots

Gómez

Fernández-Caballero

García-Varea

et al. 2014

International Journal of Advanced Robotic Systems

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This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date research in ground mobile robotics. In this sense, the paper aims at being especially helpful to both budding and experienced researchers in the areas of vision systems and mobile ground robots. The taxonomy described is devised from a novel perspective, namely in order to respond to the main questions posed when designing robotic vision systems: why?, what for?, what with?, how?, and where? The answers are derived from the most relevant techniques described in the recent literature, leading in a natural way to a series of classifications that are discussed and contextualized. The article offers a global picture of the state of the art in the area and discovers some promising research lines.

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Which Data Sources for the BIM Model?

Hajji¹,

Oulidi²

2021

Building Information Modeling for a Smart and Sustainable Urban Space

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Three-dimensional point cloud plane segmentation in both structured and unstructured environments

Cited by 91 publications

References 33 publications

An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

A Taxonomy of Vision Systems for Ground Mobile Robots

Which Data Sources for the BIM Model?

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