Accelerated patch-based planar clustering of noisy range images in indoor environments for robot mapping

Kaushik, Ravi; Xiao, Jizhong

doi:10.1016/j.robot.2011.12.001

Cited by 12 publications

(7 citation statements)

References 29 publications

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“…[13][14][15] In this article, the hybrid region growing (HRG) algorithm 16 is employed for plane segmentation. For the sake of completeness, a brief introduction is given here, please see the original article for detail.…”

Section: Point Cloud Plane Segmentationmentioning

confidence: 99%

“…Plane detection in 3-D point clouds is a complex task which has attracted increasing attention from both the computer graphics and robotics community in recent years. [13][14][15] In this article, the hybrid region growing (HRG) algorithm 16 is employed for plane segmentation. For the sake of completeness, a brief introduction is given here, please see the original article for detail.…”

Section: Point Cloud Plane Segmentationmentioning

confidence: 99%

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Pallet recognition and localization using an RGB-D camera

Xiao

Zhang

et al. 2017

International Journal of Advanced Robotic Systems

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This article reports our research results on an autonomous forklift, with the focus on pallet recognition and localization using an RGB-D camera. It is a fundamental issue for unmanned storehouses, which enables the forklift to insert the forks within the pallet's slots for loading and unloading packages. Particularly, a pallet recognition and localization approach is presented. The range image is firstly segmented into planar patches based on a region growing algorithm. Then, the segments are filtered heuristically according to the storehouse environment. Afterward, a template matching method is utilized to recognize pallets in the remained segments, based on calculating the degree of similarity at each location during sliding the templates on the segment. Once a pallet has been recognized, its pose is calculated straightforward. The article has three main contributions, that is, a low-cost RGB-D camera is employed for pallet recognition and localization, where only depth information has been utilized; using the proposed method, multiple kinds of pallets can be used at the same time, which provides a flexibility for the storehouse; and furthermore, the method has a good expansibility to allow the storehouse to adopt new pallets easily.

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Section: Point Cloud Plane Segmentationmentioning

confidence: 99%

Section: Point Cloud Plane Segmentationmentioning

confidence: 99%

Pallet recognition and localization using an RGB-D camera

Xiao

Zhang

et al. 2017

International Journal of Advanced Robotic Systems

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“…For example, in [2], a real-time plane segmentation algorithm is developed for noisy 3D point clouds acquired by less accurate and portable LIDAR. Recently, as RGB-D sensors became more available, a number of researchers have used these sensors to map planar surfaces in an indoor environment [3], [4], [5].…”

Section: Related Workmentioning

confidence: 99%

Planar building facade segmentation and mapping using appearance and geometric constraints

Lee

Song

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Segmentation and mapping of planar building facades (PBFs) can increase a robot's ability of scene understanding and localization in urban environments which are often quasi-rectilinear and GPS-challenged. PBFs are basic components of the quasi-rectilinear environment. We propose a passive vision-based PBF segmentation and mapping algorithm by combining both appearance and geometric constraints. We propose a rectilinear index which allows us to segment out planar regions using appearance data. Then we combine geometric constraints such as reprojection errors, orientation constraints, and coplanarity constraints in an optimization process to improve the mapping of PBFs. We have implemented the algorithm and tested it in comparison with state-of-the-art. The results show that our method can reduce the angular error of scene structure by an average of 82.82%.

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“…However, with RGB-D data, parametrically modeled objects (e.g., planes, spheres, cones, cylinders, and cubes) are far more reliably detectable. As a result, researchers have attempted to segment or remove large planar surfaces (e.g., walls, ceiling, and floor surfaces) as a preprocessing or fundamental step before all other algorithms (e.g., [ 27 , 28 , 29 ]).…”

Section: Introductionmentioning

confidence: 99%

Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC

Ebrahimi

Czarnuch

2021

Sensors

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Removing bounding surfaces such as walls, windows, curtains, and floor (i.e., super-surfaces) from a point cloud is a common task in a wide variety of computer vision applications (e.g., object recognition and human tracking). Popular plane segmentation methods such as Random Sample Consensus (RANSAC), are widely used to segment and remove surfaces from a point cloud. However, these estimators easily result in the incorrect association of foreground points to background bounding surfaces because of the stochasticity of randomly sampling, and the limited scene-specific knowledge used by these approaches. Additionally, identical approaches are generally used to detect bounding surfaces and surfaces that belong to foreground objects. Detecting and removing bounding surfaces in challenging (i.e., cluttered and dynamic) real-world scene can easily result in the erroneous removal of points belonging to desired foreground objects such as human bodies. To address these challenges, we introduce a novel super-surface removal technique for 3D complex indoor environments. Our method was developed to work with unorganized data captured from commercial depth sensors and supports varied sensor perspectives. We begin with preprocessing steps and dividing the input point cloud into four overlapped local regions. Then, we apply an iterative surface removal approach to all four regions to segment and remove the bounding surfaces. We evaluate the performance of our proposed method in terms of four conventional metrics: specificity, precision, recall, and F1 score, on three generated datasets representing different indoor environments. Our experimental results demonstrate that our proposed method is a robust super-surface removal and size reduction approach for complex 3D indoor environments while scoring the four evaluation metrics between 90% and 99%.

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Accelerated patch-based planar clustering of noisy range images in indoor environments for robot mapping

Cited by 12 publications

References 29 publications

Pallet recognition and localization using an RGB-D camera

Pallet recognition and localization using an RGB-D camera

Planar building facade segmentation and mapping using appearance and geometric constraints

Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC

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