Pose Estimation by Key Points Registration in Point Cloud

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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A peduncle detection method of tomato for autonomous harvesting

Complex & Intelligent Systems ◽

10.1007/s40747-021-00522-7 ◽

2021 ◽

Author(s):

Jiacheng Rong ◽

Guanglin Dai ◽

Pengbo Wang

Keyword(s):

Pose Estimation ◽

Pitch Angle ◽

Detection Method ◽

Geometric Model ◽

Average Error ◽

Long Distance ◽

End Effector ◽

Key Points ◽

Yaw Angle ◽

Cutting Point

AbstractFor automating the harvesting of bunches of tomatoes in a greenhouse, the end-effector needs to reach the exact cutting point and adaptively adjust the pose of peduncles. In this paper, a method is proposed for peduncle cutting point localization and pose estimation. Images captured in real time at a fixed long-distance are detected using the YOLOv4-Tiny detector with a precision of 92.7% and a detection speed of 0.0091 s per frame, then the YOLACT + + Network with mAP of 73.1 and a time speed of 0.109 s per frame is used to segment the close-up distance. The segmented peduncle mask is fitted to the curve using least squares and three key points on the curve are found. Finally, a geometric model is established to estimate the pose of the peduncle with an average error of 4.98° in yaw angle and 4.75° in pitch angle over the 30 sets of tests.

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Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

Remote Sensing ◽

10.3390/rs13204123 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4123

Author(s):

Hanqi Wang ◽

Zhiling Wang ◽

Linglong Lin ◽

Fengyu Xu ◽

Jie Yu ◽

...

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Component Analysis ◽

Evaluation Index ◽

Evaluation Indexes ◽

Learning Network ◽

Estimation Algorithms

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

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3-D Point Cloud Registration Using Convolutional Neural Networks

Applied Sciences ◽

10.3390/app9163273 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3273 ◽

Cited By ~ 4

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.

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