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Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud
Given the lack of scale information of the image features detected by the visual SLAM (simultaneous localization and mapping) algorithm, the accumulation of many features lacking depth information will cause scale blur, which will lead to degradation and tracking failure. In this paper, we introduce...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185257/ https://www.ncbi.nlm.nih.gov/pubmed/35684735 http://dx.doi.org/10.3390/s22114114 |
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author | Dai, Jun Li, Dongfang Li, Yanqin Zhao, Junwei Li, Wenbo Liu, Gang |
author_facet | Dai, Jun Li, Dongfang Li, Yanqin Zhao, Junwei Li, Wenbo Liu, Gang |
author_sort | Dai, Jun |
collection | PubMed |
description | Given the lack of scale information of the image features detected by the visual SLAM (simultaneous localization and mapping) algorithm, the accumulation of many features lacking depth information will cause scale blur, which will lead to degradation and tracking failure. In this paper, we introduce the lidar point cloud to provide additional depth information for the image features in estimating ego-motion to assist visual SLAM. To enhance the stability of the pose estimation, the front-end of visual SLAM based on nonlinear optimization is improved. The pole error is introduced in the pose estimation between frames, and the residuals are calculated according to whether the feature points have depth information. The residuals of features reconstruct the objective function and iteratively solve the robot’s pose. A keyframe-based method is used to optimize the pose locally in reducing the complexity of the optimization problem. The experimental results show that the improved algorithm achieves better results in the KITTI dataset and outdoor scenes. Compared with the pure visual SLAM algorithm, the trajectory error of the mobile robot is reduced by 52.7%. The LV-SLAM algorithm proposed in this paper has good adaptability and robust stability in different environments. |
format | Online Article Text |
id | pubmed-9185257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91852572022-06-11 Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud Dai, Jun Li, Dongfang Li, Yanqin Zhao, Junwei Li, Wenbo Liu, Gang Sensors (Basel) Article Given the lack of scale information of the image features detected by the visual SLAM (simultaneous localization and mapping) algorithm, the accumulation of many features lacking depth information will cause scale blur, which will lead to degradation and tracking failure. In this paper, we introduce the lidar point cloud to provide additional depth information for the image features in estimating ego-motion to assist visual SLAM. To enhance the stability of the pose estimation, the front-end of visual SLAM based on nonlinear optimization is improved. The pole error is introduced in the pose estimation between frames, and the residuals are calculated according to whether the feature points have depth information. The residuals of features reconstruct the objective function and iteratively solve the robot’s pose. A keyframe-based method is used to optimize the pose locally in reducing the complexity of the optimization problem. The experimental results show that the improved algorithm achieves better results in the KITTI dataset and outdoor scenes. Compared with the pure visual SLAM algorithm, the trajectory error of the mobile robot is reduced by 52.7%. The LV-SLAM algorithm proposed in this paper has good adaptability and robust stability in different environments. MDPI 2022-05-28 /pmc/articles/PMC9185257/ /pubmed/35684735 http://dx.doi.org/10.3390/s22114114 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dai, Jun Li, Dongfang Li, Yanqin Zhao, Junwei Li, Wenbo Liu, Gang Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud |
title | Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud |
title_full | Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud |
title_fullStr | Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud |
title_full_unstemmed | Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud |
title_short | Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud |
title_sort | mobile robot localization and mapping algorithm based on the fusion of image and laser point cloud |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185257/ https://www.ncbi.nlm.nih.gov/pubmed/35684735 http://dx.doi.org/10.3390/s22114114 |
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