LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames
In autonomous driving scenarios, the point cloud generated by LiDAR is usually considered as an accurate but sparse representation. In order to enrich the LiDAR point cloud, this paper proposes a new technique that combines spatial adjacent frames and temporal adjacent frames. To eliminate the “ghos...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/18/3640 |
| _version_ | 1797517386760323072 |
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| author | Hao Fu Hanzhang Xue Xiaochang Hu Bokai Liu |
| author_facet | Hao Fu Hanzhang Xue Xiaochang Hu Bokai Liu |
| author_sort | Hao Fu |
| collection | DOAJ |
| description | In autonomous driving scenarios, the point cloud generated by LiDAR is usually considered as an accurate but sparse representation. In order to enrich the LiDAR point cloud, this paper proposes a new technique that combines spatial adjacent frames and temporal adjacent frames. To eliminate the “ghost” artifacts caused by moving objects, a moving point identification algorithm is introduced that employs the comparison between range images. Experiments are performed on the publicly available Semantic KITTI dataset. Experimental results show that the proposed method outperforms most of the previous approaches. Compared with these previous works, the proposed method is the only method that can run in real-time for online usage. |
| first_indexed | 2024-03-10T07:15:55Z |
| format | Article |
| id | doaj.art-ec25b8caccc746bca517eab7b6554b31 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T07:15:55Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ec25b8caccc746bca517eab7b6554b312023-11-22T15:06:07ZengMDPI AGRemote Sensing2072-42922021-09-011318364010.3390/rs13183640LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent FramesHao Fu0Hanzhang Xue1Xiaochang Hu2Bokai Liu3College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaIn autonomous driving scenarios, the point cloud generated by LiDAR is usually considered as an accurate but sparse representation. In order to enrich the LiDAR point cloud, this paper proposes a new technique that combines spatial adjacent frames and temporal adjacent frames. To eliminate the “ghost” artifacts caused by moving objects, a moving point identification algorithm is introduced that employs the comparison between range images. Experiments are performed on the publicly available Semantic KITTI dataset. Experimental results show that the proposed method outperforms most of the previous approaches. Compared with these previous works, the proposed method is the only method that can run in real-time for online usage.https://www.mdpi.com/2072-4292/13/18/3640LiDAR data enrichmentmoving points identificationmulti-frame fusion |
| spellingShingle | Hao Fu Hanzhang Xue Xiaochang Hu Bokai Liu LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames Remote Sensing LiDAR data enrichment moving points identification multi-frame fusion |
| title | LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames |
| title_full | LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames |
| title_fullStr | LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames |
| title_full_unstemmed | LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames |
| title_short | LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames |
| title_sort | lidar data enrichment by fusing spatial and temporal adjacent frames |
| topic | LiDAR data enrichment moving points identification multi-frame fusion |
| url | https://www.mdpi.com/2072-4292/13/18/3640 |
| work_keys_str_mv | AT haofu lidardataenrichmentbyfusingspatialandtemporaladjacentframes AT hanzhangxue lidardataenrichmentbyfusingspatialandtemporaladjacentframes AT xiaochanghu lidardataenrichmentbyfusingspatialandtemporaladjacentframes AT bokailiu lidardataenrichmentbyfusingspatialandtemporaladjacentframes |