Fast planar clustering and polygon extraction from noisy range images acquired in indoor environments

Kaushik, Ravi; Xiao, Jizhong; Joseph, Samleo L.; Morris, William

doi:10.1109/icma.2010.5588579

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…The work of Weingarten et al [10] and Kaushik et al [13] is closely related to our novel algorithm. In [10], the point cloud was discretized into small partitions.…”

Section: Introductionmentioning

confidence: 85%

“…Like our point-based region growing algorithm, the seed is selected first, then it is extended by considering its neighbors. Compared to [13], our algorithm computes the plane parameters for the growing region whenever a new grid is added. This is advantageous for plane SLAM which cannot be performed without plane parameters.…”

Section: Grid-based Region Growingmentioning

confidence: 99%

“…It was sampled by a panning 2D laser range finder and the data is stored in a 2D array as a range image. For details on the data gathering step, the reader is referred to [13]. In Fig.…”

Section: A Feasibility Of Grid-based Region Growingmentioning

confidence: 99%

“…5. Two different view angles of one typical plane detection result using the grid-base region growing algorithm, the data is from [13].…”

Section: A Unstructured Environmentmentioning

confidence: 99%

“…The algorithm is time consuming since RANSAC is used. In [13], the point cloud was also divided into small grids, and plane parameters are computed for each grid. The grids are clustered into big planes by the so called Breadth-First-Search algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Fast plane detection for SLAM from noisy range images in both structured and unstructured environments

Xiao

Zhang

Zhang³

et al. 2011

2011 IEEE International Conference on Mechatronics and Automation

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Abstract-This paper focuses on fast plane detection in noisy range images. First, two improvements to the state-of-the-art region growing algorithm are presented to make it faster without losing precision for unstructured environments. One is to add the seed selection procedure based on local shape information to avoid blind growth. The other is to simplify the plane fitting mean square error computation complex. Second, a novel algorithm called grid-based region growing is presented for structured environments. The point cloud is divided into small patches based on neighborhood information when it is viewed as a range image. The small patch is called grid. Then the grids are classified into different categories according to their local appearance, including sparse, planar, spherical and linear. Finally, the planar grids are clustered into big patches by region growing. The plane parameters are incrementally computed whenever a new grid is added. The resulting planes can be used for 3D plane simultaneous localization and mapping (SLAM). Experimental results show promising plane detecting speed for both structured and unstructured environments.

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“…The work of Weingarten et al [10] and Kaushik et al [13] is closely related to our novel algorithm. In [10], the point cloud was discretized into small partitions.…”

Section: Introductionmentioning

confidence: 85%

Section: Grid-based Region Growingmentioning

confidence: 99%

“…It was sampled by a panning 2D laser range finder and the data is stored in a 2D array as a range image. For details on the data gathering step, the reader is referred to [13]. In Fig.…”

Section: A Feasibility Of Grid-based Region Growingmentioning

confidence: 99%

“…5. Two different view angles of one typical plane detection result using the grid-base region growing algorithm, the data is from [13].…”

Section: A Unstructured Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast plane detection for SLAM from noisy range images in both structured and unstructured environments

Xiao

Zhang

Zhang³

et al. 2011

2011 IEEE International Conference on Mechatronics and Automation

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

Xiao

Zhang

Adler

et al. 2013

Robotics and Autonomous Systems

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Plane detection in 3D point cloud using octree‐balanced density down‐sampling and iterative adaptive plane extraction

El-Sayed

Abdel-Kader

Nashaat

et al. 2018

IET Image Processing

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In this paper, a new technique for plane detection from 3D point clouds is proposed. The algorithm depends on two concepts to balance between high-accuracy and fast performance. The first is the use of a new fast octree-based balanced density down-sampling technique to reduce the number of points. The second is the fact that the number of planes in any dataset is much less than the number of the points. Random points are examined to find the 3D planes. To increase the accuracy, the system utilizes an adaptive plane extraction technique to overcome data noise. Initially, the point cloud is subdivided using octree into small cubes with a limited number of points. Then the cubes are down-sampled based on the local density of each cube. The points are explored randomly for finding a planar surface by applying principal component analysis (PCA) on the points' spherical neighborhood obtained by the down-sampled octree structure. The adaptive plane extraction is used to adjust the plane orientation to find the best position that includes the maximum number of points. Experimental results demonstrate that the proposed algorithm is capable of processing large amounts of data efficiently to produce accurate results that are robust to noise.

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Fast planar clustering and polygon extraction from noisy range images acquired in indoor environments

Cited by 9 publications

References 17 publications

Fast plane detection for SLAM from noisy range images in both structured and unstructured environments

Fast plane detection for SLAM from noisy range images in both structured and unstructured environments

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

Plane detection in 3D point cloud using octree‐balanced density down‐sampling and iterative adaptive plane extraction

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