A robust and fast method for 6DoF motion estimation from generalized 3D data

Viejo, Diego; Cazorla, Miguel

doi:10.1007/s10514-013-9354-z

Cited by 15 publications

(12 citation statements)

References 38 publications

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“…In order to find an irregular surface from the merged supervoxels in G , a thickness feature from (Viejo and Cazorla, 2014) is introduced to extract the irregular voxels, which can be measured the dispersion angle for the points in the planar patch neighbourhood, calculate by:…”

Section: Grouping Irregular Voxelsmentioning

confidence: 99%

Reconstruction of 3d Models From Point Clouds With Hybrid Representation

Dong

Yuan

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The three-dimensional (3D) reconstruction of urban buildings from point clouds has long been an active topic in applications related to human activities. However, due to the structures significantly differ in terms of complexity, the task of 3D reconstruction remains a challenging issue especially for the freeform surfaces. In this paper, we present a new reconstruction algorithm which allows the 3D-models of building as a combination of regular structures and irregular surfaces, where the regular structures are parameterized plane primitives and the irregular surfaces are expressed as meshes. The extraction of irregular surfaces starts with an over-segmented method for the unstructured point data, a region growing approach based the adjacent graph of super-voxels is then applied to collapse these super-voxels, and the freeform surfaces can be clustered from the voxels filtered by a thickness threshold. To achieve these regular planar primitives, the remaining voxels with a larger flatness will be further divided into multiscale super-voxels as basic units, and the final segmented planes are enriched and refined in a mutually reinforcing manner under the framework of a global energy optimization. We have implemented the proposed algorithms and mainly tested on two point clouds that differ in point density and urban characteristic, and experimental results on complex building structures illustrated the efficacy of the proposed framework.

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Section: Grouping Irregular Voxelsmentioning

confidence: 99%

Reconstruction of 3d Models From Point Clouds With Hybrid Representation

Dong

Yuan

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Several research works have addressed this problem using visual odometry [24] sometimes also called pose registration [25] [29], which is considered a good estimation for egomotion in the last years. These works can be considered a good starting point for automatic map building and for solving the Simultaneous Location And Mapping (SLAM) [14] problem.…”

Section: Related Workmentioning

confidence: 99%

Color smoothing for RGB-D data using entropy information

Navarrete

Viejo

Cazorla

2016

Applied Soft Computing

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RGB-D sensors are capable of providing 3D points (depth) together with color information associated with each point. These sensors suffer from different sources of noise. With some kinds of RGB-D sensors, it is possible to preprocess the color image before assigning the color information to the 3D data.However, with other kinds of sensors that is not possible: RGB-D data must be processed directly. In this paper, we compare different approaches for noise and artifacts reduction: Gaussian, mean and bilateral filter. These methods are time consuming when managing 3D data, which can be a problem with several real time applications. We propose new methods to accelerate the whole process and improve the quality of the color information using entropy information.Entropy provides a framework for speeding up the involved methods allowing certain data not to be processed if the entropy value of that data is over or under a given threshold. The experimental results provide a way to balance the quality and the acceleration of these methods. The current results show that our methods improve both the image quality and processing time, as compared to the original methods.

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“…Determining this transformation is equivalent to finding the movement performed by the robot between the poses at which data were captured. Some variants of the ICP algorithm have been proposed [5], [10], [4] and [42]. Nevertheless, ICP methods do not work correctly in the presence of outliers (features observed in one frame but not in the other) [35].…”

Section: Introductionmentioning

confidence: 99%

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

Viejo

García-Rodríguez

Cazorla

2014

Information Sciences

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The use of 3D data in mobile robotics provides valuable information about the robot's environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique.The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

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A robust and fast method for 6DoF motion estimation from generalized 3D data

Cited by 15 publications

References 38 publications

Reconstruction of 3d Models From Point Clouds With Hybrid Representation

Reconstruction of 3d Models From Point Clouds With Hybrid Representation

Color smoothing for RGB-D data using entropy information

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

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