• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.11-19
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• 2018

In this paper, we propose an algorithm for improving the accuracy and rate of convergence when two point clouds with noise and a low overlapping area are registered to each other. We make the most use of the geometric information of the underlying geometry of the point clouds with noise for better accuracy. We select a reasonable region from the noisy point cloud for registration and combine a modified acceleration algorithm to improve its speed. The conventional accuracy improvement method was not possible in a lot of noise, this paper resolves the problem by selecting the reasonable region for the registration. And this paper applies acceleration algorithm for a clone to low overlap point cloud pair. A simple algorithm is added to the conventional method, which leads to 3 or 4 times faster speed. In conclusion, this algorithm was developed to improve both the speed and accuracy of point cloud registration in noisy and low overlap case.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• Journal of Mechanical Science and Technology
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• v.19 no.12
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• pp.2213-2223
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• 2005

In order to reconstruct a full 3D human model in reverse engineering (RE), a 3D scanner needs to be placed arbitrarily around the target model to capture all part of the scanned surface. Then, acquired multiple scans must be registered and merged since each scanned data set taken from different position is just given in its own local co-ordinate system. The goal of the registration is to create a single model by aligning all individual scans. It usually consists of two sub-steps: rough and fine registration. The fine registration process can only be performed after an initial position is approximated through the rough registration. Hence an automated rough registration process is crucial to realize a completely automatic RE system. In this paper an automated rough registration method for aligning multiple scans of complex human face is presented. The proposed method automatically aligns the meshes of different scans with the information of features that are extracted from the estimated principal curvatures of triangular meshes of the human face. Then the roughly aligned scanned data sets are further precisely enhanced with a fine registration step with the recently popular Iterative Closest Point (ICP) algorithm. Some typical examples are presented and discussed to validate the proposed system.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.68-74
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• 2016

In this study, we investigated the rotational characteristics which were comprised of directionality and linearity of target registration error (TRE) as a study in advance to enhance the accuracy of contour-based registration in neuronavigation. For the experiment, two rigid head phantoms that have different faces with specially designed target frame fixed inside of the phantoms were used. Three-dimensional coordinates of facial surface point cloud and target point of the phantoms were acquired using computed tomography (CT) and 3D scanner. Iterative closest point (ICP) method was used for registration of two different point cloud and the directionality and linearity of TRE in overall head were calculated by using 3D position of targets after registration. As a result, it was represented that TRE had consistent direction in overall head region and was increased in linear fashion as distance from facial surface, but did not show high linearity. These results indicated that it is possible for decrease TRE by controlling orientation of facial surface point cloud acquired from scanner, and the prediction of TRE from surface registration error can decrease the registration accuracy in lesion. In the further studies, we have to develop the contour-based registration method for improvement of accuracy by considering rotational characteristics of TRE.