The Iterative Closest Point Registration Algorithm Based on the Normal Distribution Transformation

Shi, Xiaotao; Peng, Jianjun; Li, Jiping; Pitao, Yan; Gong, Hangyu

doi:10.1016/j.procs.2019.01.219

Cited by 49 publications

(23 citation statements)

References 8 publications

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“…However, its long computational time and need for an inclusion relationship for two-point clouds [5] seriously affects the performance of the ICP algorithm. Hence, many experts and scholars have proposed many approaches to improve the algorithm [6][7][8]. Because of this limitation of the algorithm, ICP and its variants need good initialization to avoid falling into a local minimum.…”

Section: Related Workmentioning

confidence: 99%

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Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Feng

Tang

et al. 2020

IEEE Access

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In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this paper is shown to be promising for improving the computation speed and registration accuracy. INDEX TERMS point cloud registration; feature point extraction; grey wolf optimization algorithm

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Section: Related Workmentioning

confidence: 99%

“…Formula (7) indicates the distance between an individual and the prey, while Formula (8) represents the update of the individual according to the target position. º represents the Hadamard product operation.…”

Section: ) Mathematical Modelmentioning

confidence: 99%

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Feng

Tang

et al. 2020

IEEE Access

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“…To know the L-PC position in the G-PC, the relative pose is estimated using the rotation and translation of L-PC on G-PC. This method is based on Normal Distribution Transformation, which starts from the previously calculated normals [53,54].…”

Section: Normals Calculationmentioning

confidence: 99%

Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

et al. 2020

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The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants’ geometric characteristics were extracted from the G-PC as a framework in which the robot’s positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%.

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“…Super 4PCS approach appeared to be an effective solution to handle 3-D point cloud registration when existing a low overlap and outliers. The other existing approach, the Iterative Closest Point method integrated with the Normal Distribution Transform approach (NDT-ICP), was proposed in Reference [20] with the aim of accelerating the registration speed. In addition, Liu et al [21] developed a point cloud registration algorithm based on improved digital image correlation approach, followed by the ICP method with a high accuracy, anti-noise capability and efficiency.…”

Section: Related Workmentioning

confidence: 99%

3-D Point Cloud Registration Using Convolutional Neural Networks

Chang

Pham

2019

Applied Sciences

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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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The Iterative Closest Point Registration Algorithm Based on the Normal Distribution Transformation

Cited by 49 publications

References 8 publications

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

3-D Point Cloud Registration Using Convolutional Neural Networks

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