Accurate Covariance Estimation for Pose Data From Iterative Closest Point Algorithm
One of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is reg...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Institute of Navigation
2023-03-01
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| Series: | Navigation |
| Online Access: | https://navi.ion.org/content/70/2/navi.562 |
| _version_ | 1797393350238666752 |
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| author | Rick H. Yuan Clark N. Taylor Scott L. Nykl |
| author_facet | Rick H. Yuan Clark N. Taylor Scott L. Nykl |
| author_sort | Rick H. Yuan |
| collection | DOAJ |
| description | One of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in downstream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this paper, a novel method for estimating uncertainty from sensed data is introduced. |
| first_indexed | 2024-03-09T00:01:09Z |
| format | Article |
| id | doaj.art-a2094f3d0c454cc5a2b88b7fa46fc906 |
| institution | Directory Open Access Journal |
| issn | 2161-4296 |
| language | English |
| last_indexed | 2024-03-09T00:01:09Z |
| publishDate | 2023-03-01 |
| publisher | Institute of Navigation |
| record_format | Article |
| series | Navigation |
| spelling | doaj.art-a2094f3d0c454cc5a2b88b7fa46fc9062023-12-12T17:32:42ZengInstitute of NavigationNavigation2161-42962023-03-0170210.33012/navi.562navi.562Accurate Covariance Estimation for Pose Data From Iterative Closest Point AlgorithmRick H. YuanClark N. TaylorScott L. NyklOne of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in downstream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this paper, a novel method for estimating uncertainty from sensed data is introduced.https://navi.ion.org/content/70/2/navi.562 |
| spellingShingle | Rick H. Yuan Clark N. Taylor Scott L. Nykl Accurate Covariance Estimation for Pose Data From Iterative Closest Point Algorithm Navigation |
| title | Accurate Covariance Estimation for Pose Data From Iterative Closest Point Algorithm |
| title_full | Accurate Covariance Estimation for Pose Data From Iterative Closest Point Algorithm |
| title_fullStr | Accurate Covariance Estimation for Pose Data From Iterative Closest Point Algorithm |
| title_full_unstemmed | Accurate Covariance Estimation for Pose Data From Iterative Closest Point Algorithm |
| title_short | Accurate Covariance Estimation for Pose Data From Iterative Closest Point Algorithm |
| title_sort | accurate covariance estimation for pose data from iterative closest point algorithm |
| url | https://navi.ion.org/content/70/2/navi.562 |
| work_keys_str_mv | AT rickhyuan accuratecovarianceestimationforposedatafromiterativeclosestpointalgorithm AT clarkntaylor accuratecovarianceestimationforposedatafromiterativeclosestpointalgorithm AT scottlnykl accuratecovarianceestimationforposedatafromiterativeclosestpointalgorithm |