scholarly journals Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

2018 ◽  
Vol 40 (16) ◽  
pp. 4407-4420
Author(s):  
Elvan Kuzucu ◽  
Dilan Öztürk ◽  
Mustafa Gül ◽  
Bengisu Özbay ◽  
A Mansur Arisoy ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Super Resolution ◽  
Convergence Time ◽  
Detection Methods ◽  
Range Data ◽  
Range Image ◽  
Range Images ◽  
3D Scan ◽  
Image Super Resolution ◽  
3D Lidar

As one of the most popular range detection methods, lidar is commonly used in various robotic applications. Although most robotic platforms easily adopt 2D lidar for range sensing, 3D lidar is rarely used in mobile robots, owing to its high cost. Some methods reported in the literature obtain 3D range information by rotating a single 2D lidar device. However, for most of these methods, there is a trade-off between 3D scan frequency and measurement density. Existing methods discussed in the literature for increasing the measurement density in high-frequency lidar have high time complexity and require certain conditions on data distribution. In a previous work, we showed the usability of an image super-resolution method, the Papoulis–Gerchberg (P–G) algorithm, on range data represented in the form of a greyscale image. However, the low convergence rate of the original P–G algorithm impedes its use for online applications. In this study, we advanced the P–G algorithm to drastically reduce the convergence time and improve performance by utilizing previous range images. The proposed algorithm now supports application on a mobile robot with online measurement density enhancement for 3D range images collected by rotating a 2D lidar device around its pitch axis with a high 3D scan frequency. We show illustrative examples for different scenarios to present the effectiveness of the proposed method on a 3D range sensor mounted on a mobile robot.

AN APPLICATION OF MATTING LAPLACIAN MATRIX TO RANGE IMAGE SUPER-RESOLUTION

2011 ◽  
Vol 08 (04) ◽  
pp. 273-280
Author(s):  
YUXIANG YANG ◽  
ZENGFU WANG
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Laplacian Matrix ◽  
Range Data ◽  
Range Image ◽  
Range Images ◽  
High Quality ◽  
Camera Image ◽  
Image Super Resolution

This paper describes a successful application of Matting Laplacian Matrix to the problem of generating high-resolution range images. The Matting Laplacian Matrix in this paper exploits the fact that discontinuities in range and coloring tend to co-align, which enables us to generate high-resolution range image by integrating regular camera image into the range data. Using one registered and potentially high-resolution camera image as reference, we iteratively refine the input low-resolution range image, in terms of both spatial resolution and depth precision. We show that by using such a Matting Laplacian Matrix, we can get high-quality high-resolution range images.

scholarly journals Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5185
Author(s):  
Yu Zhai ◽  
Jieyu Lei ◽  
Wenze Xia ◽  
Shaokun Han ◽  
Fei Liu ◽  
...  
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Super Resolution ◽  
Recognition System ◽  
Scene Recognition ◽  
Image Texture ◽  
Laser Radar ◽  
Range Image ◽  
Range Images ◽  
Model Library

This work introduces a super-resolution (SR) algorithm for range images on the basis of self-guided joint filtering (SGJF), adding the range information of the range image as a coefficient of the filter to reduce the influence of the intensity image texture on the super-resolved image. A range image SR recognition system is constructed to study the effect of four SR algorithms including the SGJF algorithm on the recognition of the laser radar (ladar) range image. The effects of different model library sizes, SR algorithms, SR factors and noise conditions on the recognition are tested via experiments. Results demonstrate that all tested SR algorithms can improve the recognition rate of low-resolution (low-res) range images to varying degrees and the proposed SGJF algorithm has a very good comprehensive recognition performance. Finally, suggestions for the use of SR algorithms in actual scene recognition are proposed on the basis of the experimental results.

RIDED-2D: A RULE-BASED INSTANTANEOUS DENOISING AND EDGE DETECTION METHOD FOR 2D RANGE SCAN LINE

2011 ◽  
Vol 25 (06) ◽  
pp. 807-833 ◽  
Author(s):  
JIAN WANG ◽  
ZHEN-QIANG YAO ◽  
QUAN-ZHANG AN ◽  
YAO-JIE ZHU ◽  
XUE-PING ZHANG ◽  
...  
Keyword(s):  
Edge Detection ◽  
Industrial Applications ◽  
Detection Methods ◽  
Range Image ◽  
Range Images ◽  
Basic Step ◽  
Real Time Processing ◽  
Mixed Pixels ◽  
Edge Points ◽  
Scan Line

Edge detection is often regarded as a basic step in range image processing by virtue of its crucial effect. The majority of existing edge detection methods cannot satisfy the requirement of efficiency in many industrial applications due to huge computational costs. In this paper, a novel instantaneous method, named RIDED-2D is proposed for denoising and edge detection for 2D scan line in range images. In the method, silhouettes of 2D scan line are classified into eight types by defining a few new coefficients. Several discriminant criteria on large noise filtering and edge detection are stipulated based on qualitative feature analysis on each type. Selecting some feature point candidates, a practical parameter learning method is provided to determine the threshold set, along with the implementation of an integrated algorithm by merging calculation steps. Because all the coefficients are established based on distances among the points or their ratio, RIDED-2D is inherently invariant to translation and rotation transformations. Furthermore, a forbidden region approach is proposed to eliminate interference of the mixed pixels. Key performances of RIDED-2D are evaluated in detail by including computational complexity, time expenditure, accuracy and stability. The results indicate that RIDED-2D can detect edge points accurately from several real range images, in which large noises and systematic noises are involved, and the total processing time is less than 0.1 millisecond on an ordinary PC platform using the integrated algorithm. Comparing with other state-of-the-art edge detection methods qualitatively, RIDED-2D exhibits a prominent advantage on computational efficiency. Thus, the proposed method qualifies for real-time processing in stringent industrial applications. Besides, another contribution of this paper is to introduce CPU clock counting technique to evaluate the performance of the proposed algorithm, and suggest a convenient and objective way to estimate the algorithm's time expenditure in other platforms.

COIFLET BASED METHODS FOR RANGE IMAGE PROCESSING

2007 ◽  
Vol 07 (02) ◽  
pp. 321-351
Author(s):  
M. DJEBALI ◽  
M. MELKEMI ◽  
K. MELKEMI ◽  
N. SAPIDIS
Keyword(s):  
Image Processing ◽  
Reconstruction Algorithm ◽  
Geometrical Model ◽  
Range Data ◽  
Range Image ◽  
Range Images ◽  
3D Objects ◽  
Industry Applications ◽  
Cad Cam ◽  
Range Image Processing

In industry applications, the range images are generally huge points arrays and are additively noised. They usually represent surfaces of 3D objects and are used for reverse engineering process in CAD/CAM domains. To compute the geometrical model of each surface present in the range image, we denoise and sub-sample the raw range data. Denoising allows us to avoid the adverse effects of the noise on the obtained result. Sub-sampling the raw range data leads to a low image processing overheads like those of segmentation process. Based on interpolation properties of particular wavelets named coiflets, we propose a method for smoothing noisy range images. The smoothed image keeps invariant the "topological characteristics" of the represented surfaces. Thereafter, we propose a method for sub-sampling dense range images which leads to the reduction of the amount of raw data by a factor of four. This method eliminates the "redundant" information, thus the obtained result describes the essential details (as the shape of the physical surface) of the initial range image. The smoothing and sub-sampling methods are designed to be easily integrated in any reconstruction algorithm to improve its result and reduce its overhead in spite of its high complexity.

Detection of traversable region around a mobile robot by computing terrain unevenness from the range data of a 3D laser scanner

2016 ◽  
Vol 4 (2) ◽  
pp. 107-128 ◽  
Author(s):  
Satish Kumar Reddy ◽  
Prabir K. Pal
Keyword(s):  
Mobile Robot ◽  
Region Growing ◽  
Obstacle Detection ◽  
Outdoor Environment ◽  
Research Centre ◽  
Range Data ◽  
Threshold Values ◽  
Simple Method ◽  
Content Type ◽  
3D Scan

Purpose – The purpose of this paper is to detect traversable regions surrounding a mobile robot by computing terrain unevenness using the range data obtained from a single 3D scan. Design/methodology/approach – The geometry of acquiring range data from a 3D scan is exploited to probe the terrain and extract traversable regions. Nature of terrain under each scan point is quantified in terms of an unevenness value, which is computed from the difference in range of scan point with respect to its neighbours. Both radial and transverse unevenness values are computed and compared with threshold values at every point to determine if the point belongs to a traversable region or an obstacle. A region growing algorithm spreads like a wavefront to join all traversable points into a traversable region. Findings – This simple method clearly distinguishes ground and obstacle points. The method works well even in presence of terrain slopes or when the robot experiences pitch and roll. Research limitations/implications – The method applies on single 3D scans and not on aggregated point cloud in general. Practical implications – The method has been tested on a mobile robot in outdoor environment in our research centre. Social implications – This method, along with advanced navigation schemes, can reduce human intervention in many mobile robot applications including unmanned ground vehicles. Originality/value – Range difference between scan points has been used earlier for obstacle detection, but no methodology has been developed around this concept. The authors propose a concrete method based on computation of radial and transverse unevenness at every point and detecting obstacle edges using range-dependent threshold values.

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