Ground plane detection in 3D scenes for an arbitrary camera roll rotation through "V-disparity" representation

Skulimowski, Piotr; Owczarek, M.; Strumiłło, Paweł

doi:10.15439/2017f40

Cited by 11 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the properties of the HT, this method can detect the planes with discontinuities. The ground plane detection using the depth maps captured by a stereo camera is presented in reference [12]. The camera is moving, and the system deals with the elimination of their roll angle for the correct plane segmentation.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

View full text Add to dashboard Cite

Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Many researchers are using depth images in their work (as shown in references [9][10][11][12][13]). The depth images contain the real physical sizes and distances which are possible to utilize further.…”

Section: Our Point Cloud Processing Contributionmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The paper [6] describes algorithm for the ground plane detection from depth maps captured by a stereo camera. A camera is moving and the system deals with elimination of the camera's roll angle for the correct plane segmentation.…”

Section: Introductionmentioning

confidence: 99%

Advanced plane properties by using level image

Chmelar

Beran

Chmelarova

et al. 2018

2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA)

View full text Add to dashboard Cite

This paper deals with advanced plane properties in analyzed point clouds. Planes are detected by the level connected component labeling, which is our modification of the classical algorithm for 3D data. According selected detection parameters and a scanning dimension the algorithm detects individual levels in an input point cloud. A level is presented by an image expressing the points' presence at a specific level in a space, we call it level image. The pixel size in a level image is equal to the point cloud distance quantization. This connection allows us to use image processing methods to get important properties about the analyzed 3D space, which can be difficult to get by the standard mathematical solution. Image processing methods offers a simple plane segmentation and visualization or area and perimeter estimation including statistical data description. The results show level image advantages.

show abstract

“…However, in real-world applications, ψ is not always zero and hence both angles have to be estimated independently although this is not usually a straightforward process [11]. In recent years, some passive sensor-based methods [15,5,14,16,17,18] have been proposed to estimate γ. Most of them used a single camera to acquire road data and some ideal assumptions, such as constant lane width, were made to ensure these algorithms work well [17].…”

mentioning

confidence: 99%

“…In [17] and [18], the authors assumed that the road surface is a ground plane and estimated the roll and pitch angles from a v-disparity map. In [15,5,9],…”

mentioning

confidence: 99%

A novel disparity transformation algorithm for road segmentation

Fan¹,

Bocus²,

Dahnoun³

2018

Information Processing Letters

View full text Add to dashboard Cite

The disparity information provided by stereo cameras has enabled advanced driver assistance systems to estimate road area more accurately and effectively.In this paper, a novel disparity transformation algorithm is proposed to extract road areas from dense disparity maps by making the disparity value of the road pixels become similar. The transformation is achieved using two parameters: roll angle γ and fitted disparity value d with respect to each row. To achieve a better processing efficiency, golden section search and dynamic programming are utilised to estimate γ and d, respectively. By performing a rotation around γ, the disparity distribution of each row becomes very compact. This further improves the accuracy of the road model estimation, as demonstrated by the various experimental results in this paper. Finally, the Otsu's thresholding method is applied to the transformed disparity map and the roads can be accurately segmented at pixel level.

show abstract

Ground plane detection in 3D scenes for an arbitrary camera roll rotation through "V-disparity" representation

Cited by 11 publications

References 15 publications

Advanced Methods for Point Cloud Processing and Simplification

Advanced Methods for Point Cloud Processing and Simplification

Advanced plane properties by using level image

A novel disparity transformation algorithm for road segmentation

Contact Info

Product

Resources

About