Low Overlapping Point Cloud Registration Using Line Features Detection
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 over...
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doaj-6579246f09df4d1d80e438bd224821a22020-11-25T02:18:24ZengMDPI AGRemote Sensing2072-42922019-12-011216110.3390/rs12010061rs12010061Low Overlapping Point Cloud Registration Using Line Features DetectionMiloš Prokop0Salman Ahmed Shaikh1Kyoung-Sook Kim2National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, JapanNational Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, JapanNational Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, JapanModern 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.https://www.mdpi.com/2072-4292/12/1/61point cloud registrationlow overlapping point-cloudmulti-agent cooperationline features detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Miloš Prokop Salman Ahmed Shaikh Kyoung-Sook Kim |
spellingShingle |
Miloš Prokop Salman Ahmed Shaikh Kyoung-Sook Kim Low Overlapping Point Cloud Registration Using Line Features Detection Remote Sensing point cloud registration low overlapping point-cloud multi-agent cooperation line features detection |
author_facet |
Miloš Prokop Salman Ahmed Shaikh Kyoung-Sook Kim |
author_sort |
Miloš Prokop |
title |
Low Overlapping Point Cloud Registration Using Line Features Detection |
title_short |
Low Overlapping Point Cloud Registration Using Line Features Detection |
title_full |
Low Overlapping Point Cloud Registration Using Line Features Detection |
title_fullStr |
Low Overlapping Point Cloud Registration Using Line Features Detection |
title_full_unstemmed |
Low Overlapping Point Cloud Registration Using Line Features Detection |
title_sort |
low overlapping point cloud registration using line features detection |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-12-01 |
description |
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. |
topic |
point cloud registration low overlapping point-cloud multi-agent cooperation line features detection |
url |
https://www.mdpi.com/2072-4292/12/1/61 |
work_keys_str_mv |
AT milosprokop lowoverlappingpointcloudregistrationusinglinefeaturesdetection AT salmanahmedshaikh lowoverlappingpointcloudregistrationusinglinefeaturesdetection AT kyoungsookkim lowoverlappingpointcloudregistrationusinglinefeaturesdetection |
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