LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames

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...

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Main Authors: Hao Fu, Hanzhang Xue, Xiaochang Hu, Bokai Liu
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Remote Sensing
Subjects:
LiDAR data enrichment
moving points identification
multi-frame fusion
Online Access:https://www.mdpi.com/2072-4292/13/18/3640
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record_format Article
spelling doaj-ec25b8caccc746bca517eab7b6554b312021-09-26T01:16:52ZengMDPI AGRemote Sensing2072-42922021-09-01133640364010.3390/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
collection DOAJ
language English
format Article
sources DOAJ
author Hao Fu
Hanzhang Xue
Xiaochang Hu
Bokai Liu
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
author_facet Hao Fu
Hanzhang Xue
Xiaochang Hu
Bokai Liu
author_sort Hao Fu
title LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames
title_short 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_sort lidar data enrichment by fusing spatial and temporal adjacent frames
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-09-01
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.
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
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