scholarly journals Relative Pose Determination of Uncooperative Spacecraft Based on Circle Feature

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8495
Author(s):  
Yue Liu ◽  
Shijie Zhang ◽  
Xiangtian Zhao
Keyword(s):  
Point Cloud ◽  
Feature Detection ◽  
Image Data ◽  
Coordinate Frame ◽  
Relative Navigation ◽  
Proximity Operations ◽  
Navigation Strategy ◽  
Relative Pose ◽  
Tof Camera ◽  
Fast Extraction

This paper investigates the problem of spacecraft relative navigation with respect to an unknown target during the close-proximity operations in the on-orbit service system. The serving spacecraft is equipped with a Time-of-Flight (ToF) camera for object recognition and feature detection. A fast and robust relative navigation strategy for acquisition is presented without any extra information about the target by using the natural circle features. The architecture of the proposed relative navigation strategy consists of three ingredients. First, a point cloud segmentation method based on the auxiliary gray image is developed for fast extraction of the circle feature point cloud of the target. Secondly, a new parameter fitting method of circle features is proposed including circle feature calculation by two different geometric models and results’ fusion. Finally, a specific definition of the coordinate frame system is introduced to solve the relative pose with respect to the uncooperative target. In order to validate the efficiency of the segmentation, an experimental test is conducted based on real-time image data acquired by the ToF camera. The total time consumption is saved by 94%. In addition, numerical simulations are carried out to evaluate the proposed navigation algorithm. It shows good robustness under the different levels of noises.

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.

scholarly journals Performance of Polar-Coded 3D Image Transmission over Fading Channel

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jianjun Hao ◽  
Luyao Liu ◽  
Wei Chen
Keyword(s):  
White Noise ◽  
Data Transmission ◽  
Fading Channel ◽  
Point Cloud ◽  
Gaussian White Noise ◽  
Image Data ◽  
Image Transmission ◽  
Transmission System ◽  
3D Point Cloud ◽  
3D Image

Any signal transmitted over an air-to-ground channel is corrupted by fading, noise, and interference. In this paper, a Polar-coded 3D point cloud image transmission system with fading channel is modeled, and also the simulation is performed to verify its performance in terms of 3D point cloud image data transmission over Rician channel with Gaussian white noise and overlap of Gaussian white noise + periodic pulse jamming separately. The comparison of Polar-coded scheme with RS-coded scheme in the same scenario indicates that Polar-coded system gives far better performance against AWGN noise and fading than the RS-coded system does in the case of short block length. But RS-coded scheme shows better performance on antipulse jamming than that of Polar-coded scheme, while there is no interleaving between codewords.

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

2021 ◽  
Vol 65 (1) ◽  
pp. 10501-1-10501-9
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

Abstract 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.

Automatic Registration of Urban Laser Point Cloud with Aerial Image Data Based on Straight-Lines

2015 ◽  
Vol 35 (5) ◽  
pp. 0528001
Author(s):  
何培培 He Peipei ◽  
万幼川 Wan Youchuan ◽  
杨威 Yang Wei ◽  
秦家鑫 Qin Jiaxin
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Aerial Image ◽  
Automatic Registration ◽  
Straight Lines

Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering

2013 ◽  
Vol 760-762 ◽  
pp. 1556-1561
Author(s):  
Ting Wei Du ◽  
Bo Liu
Keyword(s):  
Mixture Model ◽  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Depth Image ◽  
Cloud Data ◽  
Mixture Model Clustering ◽  
Gauss Mixture

Indoor scene understanding based on the depth image data is a cutting-edge issue in the field of three-dimensional computer vision. Taking the layout characteristics of the indoor scenes and more plane features in these scenes into account, this paper presents a depth image segmentation method based on Gauss Mixture Model clustering. First, transform the Kinect depth image data into point cloud which is in the form of discrete three-dimensional point data, and denoise and down-sample the point cloud data; second, calculate the point normal of all points in the entire point cloud, then cluster the entire normal using Gaussian Mixture Model, and finally implement the entire point clouds segmentation by RANSAC algorithm. Experimental results show that the divided regions have obvious boundaries and segmentation quality is above normal, and lay a good foundation for object recognition.

Point Cloud to CAD Model Registration Methods in Manufacturing Inspection

2006 ◽  
Vol 6 (4) ◽  
pp. 418-421 ◽  
Author(s):  
Thomas M. Tucker ◽  
Thomas R. Kurfess
Keyword(s):  
Computer Vision ◽  
Virtual World ◽  
Point Cloud ◽  
Physical World ◽  
Coordinate Frame ◽  
Cad Model ◽  
Modern Computer ◽  
Coordinate Metrology ◽  
Flow Of Information

Modern computer vision and coordinate metrology systems provide an ever-increasing flow of information from the physical world we live in to the virtual world inside computer systems. Often the coordinate system of the metrology device has a different coordinate frame from that of the existing objects in the virtual world. To rectify these differences, a process called registration is often applied. This paper uses a case study to highlight the differences between various registration techniques.

scholarly journals BADANIE MOŻLIWOŚCI WYKORZYSTANIA ZOBRAZOWAŃ DYNAMICZNYCH (WIDEO) Z NISKIEGO PUŁAPU LOTNICZEGO (BSP) DO OPRACOWAŃ FOTOGRAMETRYCZNYCH

1970 ◽  
pp. 53-64
Author(s):  
ADAM MŁYNARCZYK ◽  
SŁAWOMIR KRÓLEWICZ ◽  
PAWEŁ RUTKOWSKI
Keyword(s):  
High Altitude ◽  
Unmanned Aerial Vehicles ◽  
Point Cloud ◽  
Digital Terrain Model ◽  
Image Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Aerial Vehicles ◽  
The Creation ◽  
Recorded Image

The use of unmanned aerial vehicles is becoming more and more popular for making high-altitude and orthophotomap models. In this process, series of images are taken at specific intervals, usually lasting several seconds. This article demonstrates the ability to make models and orthophotomaps from dynamic images – video recorded from UAV. The best mutual coverage of photographs was indicated (95–96%) and the photogrammetric process for joining images was presented, through the creation of a point cloud to obtain a digital terrain model and the orotfotomap. The data was processed in 150 different variants and the usefulness of this method was demonstrated. Problems and errors that may occur during the processing of recorded image data are also described.

scholarly journals INVESTIGATIONS INTO THE QUALITY OF IMAGE-BASED POINT CLOUDS FROM UAV IMAGERY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 539-545 ◽  
Author(s):  
H.-J. Przybilla ◽  
M. Lindstaedt ◽  
T. Kersten
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Image Data ◽  
Point Clouds ◽  
Data Sets ◽  
Test Field ◽  
Data Format ◽  
Software Packages ◽  
Dense Point

<p><strong>Abstract.</strong> The quality of image-based point clouds generated from images of UAV aerial flights is subject to various influencing factors. In addition to the performance of the sensor used (a digital camera), the image data format (e.g. TIF or JPG) is another important quality parameter. At the UAV test field at the former Zollern colliery (Dortmund, Germany), set up by Bochum University of Applied Sciences, a medium-format camera from Phase One (IXU 1000) was used to capture UAV image data in RAW format. This investigation aims at evaluating the influence of the image data format on point clouds generated by a Dense Image Matching process. Furthermore, the effects of different data filters, which are part of the evaluation programs, were considered. The processing was carried out with two software packages from Agisoft and Pix4D on the basis of both generated TIF or JPG data sets. The point clouds generated are the basis for the investigation presented in this contribution. Point cloud comparisons with reference data from terrestrial laser scanning were performed on selected test areas representing object-typical surfaces (with varying surface structures). In addition to these area-based comparisons, selected linear objects (profiles) were evaluated between the different data sets. Furthermore, height point deviations from the dense point clouds were determined using check points. Differences in the results generated through the two software packages used could be detected. The reasons for these differences are filtering settings used for the generation of dense point clouds. It can also be assumed that there are differences in the algorithms for point cloud generation which are implemented in the two software packages. The slightly compressed JPG image data used for the point cloud generation did not show any significant changes in the quality of the examined point clouds compared to the uncompressed TIF data sets.</p>

Sign in / Sign up

Export Citation Format

Share Document