scholarly journals Global matching of point clouds for scan registration and loop detection

2020 ◽  
Vol 123 ◽  
pp. 103324
Author(s):  
Carlos Sánchez-Belenguer ◽  
Simone Ceriani ◽  
Pierluigi Taddei ◽  
Erik Wolfart ◽  
Vítor Sequeira
Keyword(s):  
Point Clouds ◽  
Loop Detection ◽  
Global Matching ◽  
Scan Registration

scholarly journals TEXTURE-AWARE DENSE IMAGE MATCHING USING TERNARY CENSUS TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 59-66 ◽  
Author(s):  
Han Hu ◽  
Chongtai Chen ◽  
Bo Wu ◽  
Xiaoxia Yang ◽  
Qing Zhu ◽  
...  
Keyword(s):  
Image Matching ◽  
Urban Areas ◽  
Point Clouds ◽  
Parameter Tuning ◽  
Census Transform ◽  
Urban Scenes ◽  
Dense Image ◽  
Global Matching ◽  
Matching Cost ◽  
Sharp Edges

Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods.

scholarly journals AUTOMATIC GENERATION OF BUILDING MODELS WITH LEVELS OF DETAIL 1-3

2016 ◽  
Vol XLI-B3 ◽  
pp. 649-654 ◽  
Author(s):  
W. Nguatem ◽  
M. Drauschke ◽  
H. Mayer
Keyword(s):  
Model Selection ◽  
Point Clouds ◽  
Automatic Generation ◽  
Stochastic Method ◽  
Depth Maps ◽  
Levels Of Detail ◽  
3D Point Clouds ◽  
Building Models ◽  
Global Matching

We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrate our workflow up to the export into CityGML.

A novel scan registration method based on the feature-less global descriptor – spherical entropy image

2017 ◽  
Vol 44 (4) ◽  
pp. 552-563 ◽  
Author(s):  
Bo Sun ◽  
Yadan Zeng ◽  
Houde Dai ◽  
Junhao Xiao ◽  
Jianwei Zhang
Keyword(s):  
Mobile Robotics ◽  
High Reliability ◽  
Three Dimensional ◽  
Point Clouds ◽  
Registration Method ◽  
Content Type ◽  
Spherical Fourier Transform ◽  
3D Point Clouds ◽  
3D Data Processing ◽  
Scan Registration

Purpose This paper aims to present the spherical entropy image (SEI), a novel global descriptor for the scan registration of three-dimensional (3D) point clouds. This paper also introduces a global feature-less scan registration strategy based on SEI. It is advantageous for 3D data processing in the scenarios such as mobile robotics and reverse engineering. Design/methodology/approach The descriptor works through representing the scan by a spherical function named SEI, whose properties allow to decompose the six-dimensional transformation into 3D rotation and 3D translation. The 3D rotation is estimated by the generalized convolution theorem based on the spherical Fourier transform of SEI. Then, the translation recovery is determined by phase only matched filtering. Findings No explicit features and planar segments should be contained in the input data of the method. The experimental results illustrate the parameter independence, high reliability and efficiency of the novel algorithm in registration of feature-less scans. Originality/value A novel global descriptor (SEI) for the scan registration of 3D point clouds is presented. It inherits both descriptive power of signature-based methods and robustness of histogram-based methods. A high reliability and efficiency registration method of scans based on SEI is also demonstrated.

scholarly journals MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING

2019 ◽  
Vol IV-2/W7 ◽  
pp. 55-62
Author(s):  
M. Hödel ◽  
T. Koch ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
A Priori ◽  
Depth Estimation ◽  
Point Clouds ◽  
Input Image ◽  
Depth Image ◽  
Dense Matching ◽  
Global Matching ◽  
Single Input ◽  
Monocular Depth ◽  
Image Pairs

<p><strong>Abstract.</strong> Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.</p>

scholarly journals Damage Signature Generation of Revetment Surface along Urban Rivers Using UAV-Based Mapping

2020 ◽  
Vol 9 (4) ◽  
pp. 283
Author(s):  
Ting Chen ◽  
Haiqing He ◽  
Dajun Li ◽  
Puyang An ◽  
Zhenyang Hui
Keyword(s):  
Visual Inspection ◽  
Low Cost ◽  
Region Growing ◽  
Point Clouds ◽  
Urban Rivers ◽  
Damage Recognition ◽  
Dense Matching ◽  
Detailed Surface ◽  
Dense Point ◽  
Global Matching

The all-embracing inspection of geometry structures of revetments along urban rivers using the conventional field visual inspection is technically complex and time-consuming. In this study, an approach using dense point clouds derived from low-cost unmanned aerial vehicle (UAV) photogrammetry is proposed to automatically and efficiently recognize the signatures of revetment damage. To quickly and accurately recover the finely detailed surface of a revetment, an object space-based dense matching approach, that is, region growing coupled with semi-global matching, is exploited to generate pixel-by-pixel dense point clouds for characterizing the signatures of revetment damage. Then, damage recognition is conducted using a proposed operator, that is, a self-adaptive and multiscale gradient operator, which is designed to extract the damaged regions with different sizes in the slope intensity image of the revetment. A revetment with slope protection along urban rivers is selected to evaluate the performance of damage recognition. Results indicate that the proposed approach can be considered an effective alternative to field visual inspection for revetment damage recognition along urban rivers because our method not only recovers the finely detailed surface of the revetment but also remarkably improves the accuracy of revetment damage recognition.

scholarly journals A Robust Framework for Simultaneous Localization and Mapping with Multiple Non-Repetitive Scanning Lidars

2021 ◽  
Vol 13 (10) ◽  
pp. 2015
Author(s):  
Yusheng Wang ◽  
Yidong Lou ◽  
Yi Zhang ◽  
Weiwei Song ◽  
Fei Huang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Calibration Procedure ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Scan Registration ◽  
Extraction Processes ◽  
Large Motion ◽  
Map Alignment ◽  
In Virtue Of ◽  
Fusion Framework

With the ability to provide long range, highly accurate 3D surrounding measurements, while lowering the device cost, non-repetitive scanning Livox lidars have attracted considerable interest in the last few years. They have seen a huge growth in use in the fields of robotics and autonomous vehicles. In virtue of their restricted FoV, they are prone to degeneration in feature-poor scenes and have difficulty detecting the loop. In this paper, we present a robust multi-lidar fusion framework for self-localization and mapping problems, allowing different numbers of Livox lidars and suitable for various platforms. First, an automatic calibration procedure is introduced for multiple lidars. Based on the assumption of rigidity of geometric structure, the transformation between two lidars can be configured through map alignment. Second, the raw data from different lidars are time-synchronized and sent to respective feature extraction processes. Instead of sending all the feature candidates for estimating lidar odometry, only the most informative features are selected to perform scan registration. The dynamic objects are removed in the meantime, and a novel place descriptor is integrated for enhanced loop detection. The results show that our proposed system achieved better results than single Livox lidar methods. In addition, our method outperformed novel mechanical lidar methods in challenging scenarios. Moreover, the performance in feature-less and large motion scenarios has also been verified, both with approvable accuracy.

scholarly journals COMPREHENSIVE COMPARISON OF TWO IMAGE-BASED POINT CLOUDS FROM AERIAL PHOTOS WITH AIRBORNE LIDAR FOR LARGE-SCALE MAPPING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 557-565 ◽  
Author(s):  
E. Widyaningrum ◽  
B. G. H. Gorte
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Topographic Map ◽  
Aerial Photos ◽  
3D Data ◽  
Comparison Results ◽  
Global Matching ◽  
3D Information

The integration of computer vision and photogrammetry to generate three-dimensional (3D) information from images has contributed to a wider use of point clouds, for mapping purposes. Large-scale topographic map production requires 3D data with high precision and accuracy to represent the real conditions of the earth surface. Apart from LiDAR point clouds, the image-based matching is also believed to have the ability to generate reliable and detailed point clouds from multiple-view images. In order to examine and analyze possible fusion of LiDAR and image-based matching for large-scale detailed mapping purposes, point clouds are generated by Semi Global Matching (SGM) and by Structure from Motion (SfM). In order to conduct comprehensive and fair comparison, this study uses aerial photos and LiDAR data that were acquired at the same time. Qualitative and quantitative assessments have been applied to evaluate LiDAR and image-matching point clouds data in terms of visualization, geometric accuracy, and classification result. The comparison results conclude that LiDAR is the best data for large-scale mapping.

scholarly journals Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 23 ◽  
Author(s):  
Michiel Vlaminck ◽  
Hiep Luong ◽  
Wilfried Philips
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Correction Method ◽  
Correction Algorithm ◽  
Loop Correction ◽  
Registration Method ◽  
Trade Offs ◽  
Loop Detection ◽  
3D Lidar ◽  
Correction System

In this paper, we present a complete loop detection and correction system developed for data originating from lidar scanners. Regarding detection, we propose a combination of a global point cloud matcher with a novel registration algorithm to determine loop candidates in a highly effective way. The registration method can deal with point clouds that are largely deviating in orientation while improving the efficiency over existing techniques. In addition, we accelerated the computation of the global point cloud matcher by a factor of 2–4, exploiting the GPU to its maximum. Experiments demonstrated that our combined approach more reliably detects loops in lidar data compared to other point cloud matchers as it leads to better precision–recall trade-offs: for nearly 100% recall, we gain up to 7% in precision. Finally, we present a novel loop correction algorithm that leads to an improvement by a factor of 2 on the average and median pose error, while at the same time only requires a handful of seconds to complete.

scholarly journals VIDEO-BASED POINT CLOUD GENERATION USING MULTIPLE ACTION CAMERAS

2015 ◽  
Vol XL-4/W5 ◽  
pp. 55-60 ◽  
Author(s):  
T. Teo
Keyword(s):  
Camera Calibration ◽  
Spatial Data ◽  
Point Clouds ◽  
Shift Factor ◽  
Time Shift ◽  
Time Alignment ◽  
3D Point Clouds ◽  
Important Trend ◽  
Global Matching ◽  
Multiple Frame

Due to the development of action cameras, the use of video technology for collecting geo-spatial data becomes an important trend. The objective of this study is to compare the image-mode and video-mode of multiple action cameras for 3D point clouds generation. Frame images are acquired from discrete camera stations while videos are taken from continuous trajectories. The proposed method includes five major parts: (1) camera calibration, (2) video conversion and alignment, (3) orientation modelling, (4) dense matching, and (5) evaluation. As the action cameras usually have large FOV in wide viewing mode, camera calibration plays an important role to calibrate the effect of lens distortion before image matching. Once the camera has been calibrated, the author use these action cameras to take video in an indoor environment. The videos are further converted into multiple frame images based on the frame rates. In order to overcome the time synchronous issues in between videos from different viewpoints, an additional timer APP is used to determine the time shift factor between cameras in time alignment. A structure form motion (SfM) technique is utilized to obtain the image orientations. Then, semi-global matching (SGM) algorithm is adopted to obtain dense 3D point clouds. The preliminary results indicated that the 3D points from 4K video are similar to 12MP images, but the data acquisition performance of 4K video is more efficient than 12MP digital images.

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