DV-LOAM: Direct Visual LiDAR Odometry and Mapping
Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged dir...
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doaj-246e88f9bbc449e1a61d39a2321461f02021-08-26T14:18:06ZengMDPI AGRemote Sensing2072-42922021-08-01133340334010.3390/rs13163340DV-LOAM: Direct Visual LiDAR Odometry and MappingWei Wang0Jun Liu1Chenjie Wang2Bin Luo3Cheng Zhang4State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSelf-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods.https://www.mdpi.com/2072-4292/13/16/3340Simultaneous Localization and Mappingdirect visual-LiDAR odometryloop closure detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Wang Jun Liu Chenjie Wang Bin Luo Cheng Zhang |
spellingShingle |
Wei Wang Jun Liu Chenjie Wang Bin Luo Cheng Zhang DV-LOAM: Direct Visual LiDAR Odometry and Mapping Remote Sensing Simultaneous Localization and Mapping direct visual-LiDAR odometry loop closure detection |
author_facet |
Wei Wang Jun Liu Chenjie Wang Bin Luo Cheng Zhang |
author_sort |
Wei Wang |
title |
DV-LOAM: Direct Visual LiDAR Odometry and Mapping |
title_short |
DV-LOAM: Direct Visual LiDAR Odometry and Mapping |
title_full |
DV-LOAM: Direct Visual LiDAR Odometry and Mapping |
title_fullStr |
DV-LOAM: Direct Visual LiDAR Odometry and Mapping |
title_full_unstemmed |
DV-LOAM: Direct Visual LiDAR Odometry and Mapping |
title_sort |
dv-loam: direct visual lidar odometry and mapping |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-08-01 |
description |
Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods. |
topic |
Simultaneous Localization and Mapping direct visual-LiDAR odometry loop closure detection |
url |
https://www.mdpi.com/2072-4292/13/16/3340 |
work_keys_str_mv |
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