Intelligent selection of scanning area for ICP based pose estimation of space structures

10.32920/ryerson.14661810.v1 ◽

2021 ◽

Author(s):

Kamran Shahid

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Space Structures ◽

Sensitivity Index ◽

Range Data ◽

Registration Accuracy ◽

Icp Algorithm ◽

Constraint Analysis ◽

Scanning Area ◽

Selection Of

Future autonomous satellite repair missions would benefit from higher accuracy pose estimates of target satellites. Constraint analysis provides a sensitivity index which can be used as a registration accuracy predictor. It was shown that point cloud configurations with higher values of this index returned more accurate pose estimates than unstable configurations with lower index values. Registration tests were conducted on four satellite geometries using synthetic range data. These results elucidate a means of determining the optimal scanning area of a given satellite for registration with the Iterative Closest Point (ICP) algorithm to return a highly accurate pose estimate.

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941.v1 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

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Pose Estimation of Non-Cooperative Space Targets Based on Cross-Source Point Cloud Fusion

Remote Sensing ◽

10.3390/rs13214239 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4239

Author(s):

Jie Li ◽

Yiqi Zhuang ◽

Qi Peng ◽

Liang Zhao

Keyword(s):

Pose Estimation ◽

Measurement System ◽

Point Cloud ◽

Conformal Geometry ◽

Unscented Kalman Filter ◽

Estimation Error ◽

Point Clouds ◽

Fusion Algorithm ◽

Registration Accuracy ◽

Source Point

On-orbit space technology is used for tasks such as the relative navigation of non-cooperative targets, rendezvous and docking, on-orbit assembly, and space debris removal. In particular, the pose estimation of space non-cooperative targets is a prerequisite for studying these applications. The capabilities of a single sensor are limited, making it difficult to achieve high accuracy in the measurement range. Against this backdrop, a non-cooperative target pose measurement system fused with multi-source sensors was designed in this study. First, a cross-source point cloud fusion algorithm was developed. This algorithm uses the unified and simplified expression of geometric elements in conformal geometry algebra, breaks the traditional point-to-point correspondence, and constructs matching relationships between points and spheres. Next, for the fused point cloud, we proposed a plane clustering-method-based CGA to eliminate point cloud diffusion and then reconstruct the 3D contour model. Finally, we used a twistor along with the Clohessy–Wiltshire equation to obtain the posture and other motion parameters of the non-cooperative target through the unscented Kalman filter. In both the numerical simulations and the semi-physical experiments, the proposed measurement system met the requirements for non-cooperative target measurement accuracy, and the estimation error of the angle of the rotating spindle was 30% lower than that of other, previously studied methods. The proposed cross-source point cloud fusion algorithm can achieve high registration accuracy for point clouds with different densities and small overlap rates.

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TLS POINT CLOUD REGISTRATION BASED ON ICP ALGORITHM USING POINT QUALITY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-963-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 963-968 ◽

Cited By ~ 1

Author(s):

H. Date ◽

E. Wakisaka ◽

Y. Moribe ◽

S. Kanai

Keyword(s):

Point Cloud ◽

Incident Angle ◽

Point Clouds ◽

Outdoor Environment ◽

Simple Extension ◽

Registration Accuracy ◽

Icp Algorithm ◽

Extension Method ◽

Registration Errors ◽

Efficient Matching

<p><strong>Abstract.</strong> A simple extension method of the ICP algorithm using point quality based on the distance and incident angle is presented for improving the registration accuracy of TLS point clouds. First, using a structured point cloud representation, the distance and incident angle of each point are calculated. Then, quality measures for the distance and incident angle are defined, considering the relationship between them and the scanning errors. Finally, an extension method of the ICP algorithm by the weighting using the estimated point qualities is described. A method for efficient matching using point properties is also introduced. The proposed method is applied to two datasets of TLS point clouds of a small plant and an outdoor environment, and the effectiveness is evaluated by comparing registration errors with the ground truths obtained using the scan target and a total station.</p>

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Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

Sensors ◽

10.3390/s20010237 ◽

2019 ◽

Vol 20 (1) ◽

pp. 237 ◽

Cited By ~ 7

Author(s):

Xuyou Li ◽

Shitong Du ◽

Guangchun Li ◽

Haoyu Li

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Large Scale ◽

Degree Of Freedom ◽

Mobile Systems ◽

Estimation Accuracy ◽

Scan Matching ◽

Icp Algorithm ◽

Localization And Mapping ◽

Six Degree Of Freedom

Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

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Target Design For Lida-Based ICP Pose Estimation For Space Vision Tasks

10.32920/ryerson.14648208.v1 ◽

2021 ◽

Author(s):

Aradhana Choudhuri

Keyword(s):

Pose Estimation ◽

Design Methodology ◽

Ccd Camera ◽

Design Tool ◽

Shape Design ◽

Icp Algorithm ◽

Shape Constraint ◽

Constraint Analysis ◽

Eigen Value ◽

Noise Amplification

The goal of this thesis is to develop a methodology for designing 3D target shapes for accurate LIDAR pose estimation. Scanned from a range of views, this shape can be attached to the surface of a spacecraft and deliver accurate pose scanned. It would act as an LIDAR- based analogue to fiducial markers placed on the surface and viewed by CCD camera(s). Continuum Shape Constraint Analysis (CSCA) which assesses shapes for pose estimation and measures the performance of the Iterative Closest Point (ICP) Algorithm is used as a shape design tool. CSCA directly assesses the sensitivity of pose error to variation in viewing direction. Three of the CSCA measures, Noise Amplification Index, Minimal Eigen-value and Expectivity Index, were compared, and Expectivity Index was shown to be the best index to use as shape design tool. Using CSCA and numerical simulations, a Cuboctahedron was shown to be an optimal shape which delivers an accurate pose when viewed from all angles and the nitial pose guess is close to the true poses. Separate from Constraint Analysis, the problem of shape ambiguity was addressed using numerical tools. The Cuboctahedron was modified in order to resolve shape ambiguity - the tendency of the ICP algorithm to converge with low registration error on a pose configuration geometrically identical, but actually different from a “true pose”. The numerical characteristics of geometrical ambiguity were studied, and a heuristic design methodology to reduce shape ambiguity was developed and is presented in this thesis. A Reduced Ambiguity Cuboctahedron is the resultant shape that delivers an accurate pose from all views and does not suffer from shape ambiguity. The shapes were subjected to simulation and experimental validation. They were manufactured using 3D Rapid Prototyper, and a NEPTEC Design Group TriDAR Scanner was used to obtain experimental data for three shapes: the Tetrahedron, Cuboctahedron, and reduced Ambiguity Cuboctahedron. The Tetrahedron, which has poorly constrained views, was included in the testing process as a comparison shape. The simulation and experimental results were congruent, and validated the design methodology and the designed shapes.

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Target Design For Lida-Based ICP Pose Estimation For Space Vision Tasks

10.32920/ryerson.14648208 ◽

2021 ◽

Author(s):

Aradhana Choudhuri

Keyword(s):

Pose Estimation ◽

Design Methodology ◽

Ccd Camera ◽

Design Tool ◽

Shape Design ◽

Icp Algorithm ◽

Shape Constraint ◽

Constraint Analysis ◽

Eigen Value ◽

Noise Amplification

The goal of this thesis is to develop a methodology for designing 3D target shapes for accurate LIDAR pose estimation. Scanned from a range of views, this shape can be attached to the surface of a spacecraft and deliver accurate pose scanned. It would act as an LIDAR- based analogue to fiducial markers placed on the surface and viewed by CCD camera(s). Continuum Shape Constraint Analysis (CSCA) which assesses shapes for pose estimation and measures the performance of the Iterative Closest Point (ICP) Algorithm is used as a shape design tool. CSCA directly assesses the sensitivity of pose error to variation in viewing direction. Three of the CSCA measures, Noise Amplification Index, Minimal Eigen-value and Expectivity Index, were compared, and Expectivity Index was shown to be the best index to use as shape design tool. Using CSCA and numerical simulations, a Cuboctahedron was shown to be an optimal shape which delivers an accurate pose when viewed from all angles and the nitial pose guess is close to the true poses. Separate from Constraint Analysis, the problem of shape ambiguity was addressed using numerical tools. The Cuboctahedron was modified in order to resolve shape ambiguity - the tendency of the ICP algorithm to converge with low registration error on a pose configuration geometrically identical, but actually different from a “true pose”. The numerical characteristics of geometrical ambiguity were studied, and a heuristic design methodology to reduce shape ambiguity was developed and is presented in this thesis. A Reduced Ambiguity Cuboctahedron is the resultant shape that delivers an accurate pose from all views and does not suffer from shape ambiguity. The shapes were subjected to simulation and experimental validation. They were manufactured using 3D Rapid Prototyper, and a NEPTEC Design Group TriDAR Scanner was used to obtain experimental data for three shapes: the Tetrahedron, Cuboctahedron, and reduced Ambiguity Cuboctahedron. The Tetrahedron, which has poorly constrained views, was included in the testing process as a comparison shape. The simulation and experimental results were congruent, and validated the design methodology and the designed shapes.

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

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

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.

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OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽

10.3390/ma14061563 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1563

Author(s):

Ruibing Wu ◽

Ziping Yu ◽

Donghong Ding ◽

Qinghua Lu ◽

Zengxi Pan ◽

...

Keyword(s):

Additive Manufacturing ◽

Point Cloud ◽

Point Clouds ◽

Iterative Closest Point ◽

Multiple Point ◽

Icp Algorithm ◽

Registration Algorithm ◽

Cloud Models ◽

Comparison Results ◽

Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

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