scholarly journals Attitude Mounting Misalignment Estimation Method for the Calibration of UAV LiDAR System by using a TIN-based Corresponding Model

2020 ◽  
Vol 1 (1) ◽  
pp. 21-27
Author(s):  
Daniel Dos Santos ◽  
Leonardo Filho ◽  
Paulo De Oliveira Jr ◽  
Henrique De Oliveira
Keyword(s):  
Autonomous Vehicle ◽  
Estimation Method ◽  
Point Clouds ◽  
Estimation Methods ◽  
Lidar System ◽  
Scale Invariant ◽  
Keypoint Matching ◽  
Coarse Registration ◽  
Triangular Irregular Network ◽  
Feature Transform

In traditional attitude mounting misalignment estimation methods for the calibration of unmanned autonomous vehicle (UAV) based light detection and ranging (LiDAR) system, signalized targets and iterative corresponding models are required, which makes it highly cost and computationally time-consuming. This paper presents an attitude mounting misalignment estimation (AMME) method for the calibration of UAV LiDAR system. The proposed method is divided into the coarse registration of LiDAR strips and the estimation of the attitude mounting misalignment. Firstly, 3D keypoints are extracted in the point clouds using the scale-invariant feature transform (SIFT) algorithm. Afterwards, the point feature transform (PFH) descriptor is used for 3D keypoint matching. Then, the coarse registration is executed. In the second part of the contribution, the systematic errors in the attitude mounting misalignment are estimated by incorporating the proposed triangular irregular network (TIN) corresponding model into the calibration modelling. Using the TIN-based corresponding model saves time and cost for AMME method. Furthermore, it provides two important effects: practical and computational, as no designed calibration boards, segmentation and iterative matching are needed. The performance of the proposed method is demonstrated under an UAV LiDAR data onboarded with lightweight navigation sensors. The experimental results show the efficacy of the method in comparison with a state-of-the-art method.

scholarly journals Efficient Coarse Registration of Pairwise TLS Point Clouds Using Ortho Projected Feature Images

2020 ◽  
Vol 9 (4) ◽  
pp. 255
Author(s):  
Hua Liu ◽  
Xiaoming Zhang ◽  
Yuancheng Xu ◽  
Xiaoyong Chen
Keyword(s):  
Parameter Estimation ◽  
Degrees Of Freedom ◽  
High Efficiency ◽  
Point Clouds ◽  
Azimuth Angle ◽  
Translation Vector ◽  
Scale Invariant ◽  
Registration Method ◽  
Coarse Registration ◽  
Vertical Translation

The degree of automation and efficiency are among the most important factors that influence the availability of Terrestrial light detection and ranging (LiDAR) Scanning (TLS) registration algorithms. This paper proposes an Ortho Projected Feature Images (OPFI) based 4 Degrees of Freedom (DOF) coarse registration method, which is fully automated and with high efficiency, for TLS point clouds acquired using leveled or inclination compensated LiDAR scanners. The proposed 4DOF registration algorithm decomposes the parameter estimation into two parts: (1) the parameter estimation of horizontal translation vector and azimuth angle; and (2) the parameter estimation of the vertical translation vector. The parameter estimation of the horizontal translation vector and the azimuth angle is achieved by ortho projecting the TLS point clouds into feature images and registering the ortho projected feature images by Scale Invariant Feature Transform (SIFT) key points and descriptors. The vertical translation vector is estimated using the height difference of source points and target points in the overlapping regions after horizontally aligned. Three real TLS datasets captured by the Riegl VZ-400 and the Trimble SX10 and one simulated dataset were used to validate the proposed method. The proposed method was compared with four state-of-the-art 4DOF registration methods. The experimental results showed that: (1) the accuracy of the proposed coarse registration method ranges from 0.02 m to 0.07 m in horizontal and 0.01 m to 0.02 m in elevation, which is at centimeter-level and sufficient for fine registration; and (2) as many as 120 million points can be registered in less than 50 s, which is much faster than the compared methods.

scholarly journals Investigating Performance Of Airborne Lidar Data Filtering With Triangular Irregular Network (TIN) Algorithm

2014 ◽  
Vol XL-7 ◽  
pp. 199-202 ◽  
Author(s):  
M. Uysal ◽  
N. Polat
Keyword(s):  
Urban Area ◽  
Rural Area ◽  
Land Surface ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar System ◽  
Filtering Algorithm ◽  
Topographic Features ◽  
Comparison Results ◽  
Triangular Irregular Network

Digital Elevation Model (DEM) is an important topographic product and essential demand for many applications. Traditional methods for creating DEM are very costly and time consuming because of land surveying. In time, Photogrammetry has become one of the major methods to generate DEM. Recently, airborne Light Detection and Ranging (LIDAR) system has become a powerful way to produce a DEM due to advantage of collecting three-dimensional information very effectively over a large area by means of precision and time. <br><br> Airborne LIDAR system collects information not only from land surface but also from every object between plane and terrain that can reflect the laser beam. So filtering out nonground points from raw point clouds is the major step of DEM generation. There are many filtering algorithm due to several factors that affect the filtering prosedures. The performanses of these filters change based on the topographic features of area.One of these algorithm is called Triangular Irregular Network (TIN). <br><br> In this study the TIN algorithm is used to filter Lidar point cloud that are collected from two different sites. While one of these sites is a rural area, the other site is an urban area; therefore these sites have different topographic features. In addition, the reference DEMs are available for these sites. In order to test the performance of TIN algorithm, the Lidar point clouds are filtered and used to generate DEM for the sites. Finally, the generated DEM are compared with the reference DEM for each site. The comparison results show that the TIN filtering algorithm perform more effectively in urban area than rural area in terms of correlations with reference DEMs.

ADAPTION OF INVARIANT FEATURES IN IMAGE FOR POINT CLOUDS REGISTRATION

2015 ◽  
Vol 75 (10) ◽  
Author(s):  
Mohd Azwan Abbas ◽  
Halim Setan ◽  
Zulkepli Majid ◽  
Albert K. Chong ◽  
Lau Chong Luh ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Scale Invariant ◽  
Registration Method ◽  
Flat Surfaces ◽  
Invariant Features ◽  
Limited Class ◽  
Coarse Registration ◽  
Straight Lines ◽  
Scale Invariant Feature

Currently, coarse registration methods for scanner are required heavy operator intervention either before or after scanning process. There also have an automatic registration method but only applicable to a limited class of objects (e.g. straight lines and flat surfaces). This study is devoted to a search of a computationally feasible automatic coarse registration method with a broad range of applicability. Nowadays, most laser scanner systems are supplied with a camera, such that the scanned data can also be photographed. The proposed approach will exploit the invariant features detected from image to associate point cloud registration. Three types of detectors are included: scale invariant feature transform (SIFT), 2) Harris affine, and 3) maximally stable extremal regions (MSER). All detected features will transform into the laser scanner coordinate system, and their performance is measured based on the number of corresponding points. Several objects with different observation techniques were performed to evaluate the capability of proposed approach and also to evaluate the performance of selected detectors.  

scholarly journals SCALE INVARIANT FEATURE TRANSFORM PLUS HUE FEATURE

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 27-32
Author(s):  
M. B.Daneshvar
Keyword(s):  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Keypoint Matching ◽  
Matching Process ◽  
Invariant Feature ◽  
Speed Up ◽  
Euclidean Distances ◽  
Feature Transform ◽  
The Difference ◽  
Scale Invariant Feature

This paper presents an enhanced method for extracting invariant features from images based on Scale Invariant Feature Transform (SIFT). Although SIFT features are invariant to image scale and rotation, additive noise, and changes in illumination but we think this algorithm suffers from excess keypoints. Besides, by adding the hue feature, which is extracted from combination of hue and illumination values in HSI colour space version of the target image, the proposed algorithm can speed up the matching phase. Therefore, we proposed the Scale Invariant Feature Transform plus Hue (SIFTH) that can remove the excess keypoints based on their Euclidean distances and adding hue to feature vector to speed up the matching process which is the aim of feature extraction. In this paper we use the difference of hue features and the Mean Square Error (MSE) of orientation histograms to find the most similar keypoint to the under processing keypoint. The keypoint matching method can identify correct keypoint among clutter and occlusion robustly while achieving real-time performance and it will result a similarity factor of two keypoints. Moreover removing excess keypoint by SIFTH algorithm helps the matching algorithm to achieve this goal.

scholarly journals A Survey on Novel Estimation Approach of Motion Controllers for Self-Driving Cars

2021 ◽  
Vol 2 (4) ◽  
pp. 211-219
Author(s):  
Vinothkanna R
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Estimation Method ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Real Time Traffic ◽  
Traffic Uncertainty ◽  
Research Article ◽  
Uncertainty Control

The motion planning framework is one of the challenging tasks in autonomous driving cars. During motion planning, predicting of trajectory is computed by Gaussian propagation. Recently, the localization uncertainty control will be estimating by Gaussian framework. This estimation suffers from real time constraint distribution for (Global Positioning System) GPS error. In this research article compared novel motion planning methods and concluding the suitable estimating algorithm depends on the two different real time traffic conditions. One is the realistic unusual traffic and complex target is another one. The real time platform is used to measure the several estimation methods for motion planning. Our research article is that comparing novel estimation methods in two different real time environments and an identifying better estimation method for that. Our suggesting idea is that the autonomous vehicle uncertainty control is estimating by modified version of action based coarse trajectory planning. Our suggesting framework permits the planner to avoid complex and unusual traffic (uncertainty condition) efficiently. Our proposed case studies offer to choose effectiveness framework for complex mode of surrounding environment.

scholarly journals A Spherical Model Based Keypoint Descriptor and Matching Algorithm for Omnidirectional Images

2014 ◽  
Vol 6 ◽  
pp. 154376 ◽  
Author(s):  
Guofeng Tong ◽  
Xue Chen ◽  
Ning Ye
Keyword(s):  
Radial Direction ◽  
Nonlinear Distortion ◽  
Spherical Model ◽  
Scale Invariant ◽  
Keypoint Matching ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Omnidirectional Images ◽  
Scale Invariant Feature

Omnidirectional images generally have nonlinear distortion in radial direction. Unfortunately, traditional algorithms such as scale-invariant feature transform (SIFT) and Descriptor-Nets (D-Nets) do not work well in matching omnidirectional images just because they are incapable of dealing with the distortion. In order to solve this problem, a new voting algorithm is proposed based on the spherical model and the D-Nets algorithm. Because the spherical-based keypoint descriptor contains the distortion information of omnidirectional images, the proposed matching algorithm is invariant to distortion. Keypoint matching experiments are performed on three pairs of omnidirectional images, and comparison is made among the proposed algorithm, the SIFT and the D-Nets. The result shows that the proposed algorithm is more robust and more precise than the SIFT, and the D-Nets in matching omnidirectional images. Comparing with the SIFT and the D-Nets, the proposed algorithm has two main advantages: (a) there are more real matching keypoints; (b) the coverage range of the matching keypoints is wider, including the seriously distorted areas.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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