Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds

2020 ◽  
Author(s):  
Jing Dao Chen ◽  
Pileun Kim ◽  
Dong-Ik Sun ◽  
Chang-Soo Han ◽  
Yong Han Ahn ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Point Clouds ◽  
Construction Equipment

scholarly journals A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4064
Author(s):  
Can Li ◽  
Ping Chen ◽  
Xin Xu ◽  
Xinyu Wang ◽  
Aijun Yin
Keyword(s):  
Pose Estimation ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Admittance Control ◽  
3D Vision ◽  
Axis Orientation ◽  
3D Point Clouds ◽  
Object Pose Estimation ◽  
Traditional Approaches ◽  
Coarse To Fine

In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

scholarly journals Principal Component Analysis For ICP Pose Estimation Of Space Structures

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.

scholarly journals Principal Component Analysis For ICP Pose Estimation Of Space Structures

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.

Initial registration of point clouds using camera pose estimation

2017 ◽  
Vol 25 (6) ◽  
pp. 1635-1644
Author(s):  
郭清达 GUO Qing-da ◽  
全燕鸣 QUAN Yan-ming ◽  
姜长城 JIANG Chang-cheng ◽  
陈健武 CHEN Jian-wu
Keyword(s):  
Pose Estimation ◽  
Point Clouds ◽  
Camera Pose Estimation ◽  
Camera Pose

scholarly journals 3-D Point Cloud Registration Using Convolutional Neural Networks

2019 ◽  
Vol 9 (16) ◽  
pp. 3273 ◽  
Author(s):  
Wen-Chung Chang ◽  
Van-Toan Pham
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Full Range ◽  
Point Clouds ◽  
Fixed Time ◽  
Training Data ◽  
Data Sets ◽  
Point Cloud Registration ◽  
Cloud Data ◽  
Model Point

This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision.

An Integrated Framework for 3-D Modeling, Object Detection, and Pose Estimation From Point-Clouds

2015 ◽  
Vol 64 (3) ◽  
pp. 683-693 ◽  
Author(s):  
Yulan Guo ◽  
Mohammed Bennamoun ◽  
Ferdous Sohel ◽  
Min Lu ◽  
Jianwei Wan
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Point Clouds ◽  
Integrated Framework

scholarly journals A framework for non-ICP lidar-based pose estimation using an optimally constrained 3D target

2021 ◽  
Author(s):  
Pierre Saint-Cyr
Keyword(s):  
Pose Estimation ◽  
Full Range ◽  
Point Clouds ◽  
Initial Guess ◽  
Measurement Noise ◽  
Lidar Data ◽  
Lidar Measurement ◽  
Camera System ◽  
Icp Algorithm ◽  
Scan Data

This thesis describes a non-ICP-based framework fohr [sic] the computation of a pose estimate of a special target shape from raw LIDAR scan data. In previous work, an ideal unambiguously-shaped 3D target (the Reduced Ambiguity Cuboctahedron, or RAC) was designed for use in LIDAR-based pose estimation. The RAC was designed to be used in an ICP algorithm, without an initial guess at the pose. This property is, however, not robust to LIDAR measurement noise and data artefacts. The pose estimation technique described in the present work is based upon the geometric non-ambiguity criteria used originally to design the target, and is robust to the aforementioned LIDAR data characteristics. This technique has been tested using simulated point clouds representing a full range of views of the RAC. The technique has been validated using real LIDAR scans of the RAC, generated at Neptec's Ottawa facility with their Laser Camera System (LCS). Experimental results using LCS data show that pose estimates can be generated with mean errors (relative to ICP) of 1.03 [deg] and 1.08 [mm], having standard deviations of 0.56 [deg] and 0.67 [mm] respectively.

scholarly journals A framework for non-ICP lidar-based pose estimation using an optimally constrained 3D target

2021 ◽  
Author(s):  
Pierre Saint-Cyr
Keyword(s):  
Pose Estimation ◽  
Full Range ◽  
Point Clouds ◽  
Initial Guess ◽  
Measurement Noise ◽  
Lidar Data ◽  
Lidar Measurement ◽  
Camera System ◽  
Icp Algorithm ◽  
Scan Data

This thesis describes a non-ICP-based framework fohr [sic] the computation of a pose estimate of a special target shape from raw LIDAR scan data. In previous work, an ideal unambiguously-shaped 3D target (the Reduced Ambiguity Cuboctahedron, or RAC) was designed for use in LIDAR-based pose estimation. The RAC was designed to be used in an ICP algorithm, without an initial guess at the pose. This property is, however, not robust to LIDAR measurement noise and data artefacts. The pose estimation technique described in the present work is based upon the geometric non-ambiguity criteria used originally to design the target, and is robust to the aforementioned LIDAR data characteristics. This technique has been tested using simulated point clouds representing a full range of views of the RAC. The technique has been validated using real LIDAR scans of the RAC, generated at Neptec's Ottawa facility with their Laser Camera System (LCS). Experimental results using LCS data show that pose estimates can be generated with mean errors (relative to ICP) of 1.03 [deg] and 1.08 [mm], having standard deviations of 0.56 [deg] and 0.67 [mm] respectively.

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

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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