An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features

He, Ying; Liang, Bin; Yang, Jun; Li, Shunzhi; He, Jin

doi:10.3390/s17081862

Cited by 175 publications

(89 citation statements)

References 23 publications

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“…This explicitly introduces the general assumption of a point-cloud registration problem; that the size of an overlapping area is very large and only a minor correction in translation and rotation is sought [4]. Generally, the solution in a high overlapping point-cloud consists of keypoints detection [7][8][9], descriptors calculation [10][11][12] around each of the keypoints and running an Iterative Closest Point (ICP) algorithm [13,14] to find a transformation that pair-wise matches the individual descriptors. When the overlapping area is small, as in our case, it is difficult to reliably find the matching keypoints in the two-point-clouds, which is an essential step in almost all of the existing point-cloud registration approaches.…”

Section: Related Workmentioning

confidence: 99%

Low Overlapping Point Cloud Registration Using Line Features Detection

2019

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Modern robotic exploratory strategies assume multi-agent cooperation that raises a need for an effective exchange of acquired scans of the environment with the absence of a reliable global positioning system. In such situations, agents compare the scans of the outside world to determine if they overlap in some region, and if they do so, they determine the right matching between them. The process of matching multiple point-cloud scans is called point-cloud registration. Using the existing point-cloud registration approaches, a good match between any two-point-clouds is achieved if and only if there exists a large overlap between them, however, this limits the advantage of using multiple robots, for instance, for time-effective 3D mapping. Hence, a point-cloud registration approach is highly desirable if it can work with low overlapping scans. This work proposes a novel solution for the point-cloud registration problem with a very low overlapping area between the two scans. In doing so, no initial relative positions of the point-clouds are assumed. Most of the state-of-the-art point-cloud registration approaches iteratively match keypoints in the scans, which is computationally expensive. In contrast to the traditional approaches, a more efficient line-features-based point-cloud registration approach is proposed in this work. This approach, besides reducing the computational cost, avoids the problem of high false-positive rate of existing keypoint detection algorithms, which becomes especially significant in low overlapping point-cloud registration. The effectiveness of the proposed approach is demonstrated with the help of experiments.

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Section: Related Workmentioning

confidence: 99%

Low Overlapping Point Cloud Registration Using Line Features Detection

2019

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“…Table 1 shows the comparison between ICP and cross-correlation algorithm applied on the 117 pixel × 179 pixel image. ICP is a type of point cloud data registration method that can achieve the accurate registration of large amounts of data [36]. The three-dimensional (3D) ICP is applied to 2D DPIV image registration simply by projecting the 2D gray image into 3D space, as shown in Figure 4.…”

Section: Icp Algorithmmentioning

confidence: 99%

Flow Velocity Field Measurement of Vertical Upward Oil–Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS

et al. 2019

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Flow velocity field measurement is important for analyzing flow characteristics of oil-water two-phase immiscible flow in vertical well. Digital particle image velocimetry (DPIV) is an effective velocity field measurement method that has overcome single point measurement limitation of traditional instruments. However, multiphase flow velocity fields generated by DPIV are often accompanied by local false vectors caused by image mismatching, which leads to measurement results with low accuracy. In this paper, the reasons for oil-water two-phase immiscible flow image mismatching in inner diameter 125 mm vertical pipe is identified by studying the DPIV calculation process. This is mainly caused by image noise and poor window following performance that results from poor deformation performance of the interrogation window. To improve deformation performance of the interrogation window, and thus improve the accuracy of the algorithm, iterative closest point (ICP) and moving least squares (MLS) are introduced into the window deformation iterative multigrid algorithm in DPIV postprocessing algorithm. The simulation showed that the improved DPIV algorithm had good matching performance, and thus the false vector was reduced. The experimental results showed that, in light of the present investigation, on average, the improved DPIV algorithm is found to yield an accuracy improvement of~6%; the measurement uncertainty and reproducibility of the improved DPIV algorithm were 0.149 × 10 −3 m/s and 1.98%, respectively.

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“…Object detection, which is identification of the position and category of objects in a scene, is a relatively simpler task compared to identifying the rotation or orientation of objects. For detecting rotation, there exists methods that are based on deep neural network (Kanezaki, ), iterative closest point (ICP) (He, Liang, Yang, Li & He, ), or complete 3D models of the object (Hinterstoisser et al, ). Such method provides complete rotation detection of objects but have certain drawbacks.…”

Section: Target Detection and Tool Acquisitionmentioning

confidence: 99%

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

Chan¹,

Mizohana²,

Chen³

et al. 2018

Journal of Field Robotics

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The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.

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An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features

Cited by 175 publications

References 23 publications

Low Overlapping Point Cloud Registration Using Line Features Detection

Low Overlapping Point Cloud Registration Using Line Features Detection

Flow Velocity Field Measurement of Vertical Upward Oil–Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

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