Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching Positioning
Light detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation s...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/8/1457 |
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| author | Guoliang Liu Wang Gao Shuguo Pan |
| author_facet | Guoliang Liu Wang Gao Shuguo Pan |
| author_sort | Guoliang Liu |
| collection | DOAJ |
| description | Light detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation satellite system (GNSS) observations is usually considered to be closely related to the elevation angle factor. However, in the LiDAR point cloud feature matching positioning model, the analysis of factors affecting the random measurement error of observations is unsophisticated, which limits the ability of LiDAR sensors to estimate pose parameters. Therefore, this work draws on the random measurement error analysis method of GNSS observations to study the impact of factors such as distance, angle, and feature accuracy on the random measurement error of LiDAR. The experimental results show that feature accuracy is the main factor affecting the measurement error in the LiDAR point cloud feature matching positioning model, compared with distance and angle factors, even under different sensor specifications, point cloud densities, prior maps, and urban road scenes. Furthermore, a simple random measurement error model based on the feature accuracy factor is used to verify the effect of parameter estimation, and the results show that the random error model can effectively reduce the error of pose parameter estimation, with an improvement effect of about 50%. |
| format | Article |
| id | doaj-art-778110c3cd734f478a75e5a84f7955cf |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-778110c3cd734f478a75e5a84f7955cf2025-08-20T02:28:20ZengMDPI AGRemote Sensing2072-42922025-04-01178145710.3390/rs17081457Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching PositioningGuoliang Liu0Wang Gao1Shuguo Pan2School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaLight detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation satellite system (GNSS) observations is usually considered to be closely related to the elevation angle factor. However, in the LiDAR point cloud feature matching positioning model, the analysis of factors affecting the random measurement error of observations is unsophisticated, which limits the ability of LiDAR sensors to estimate pose parameters. Therefore, this work draws on the random measurement error analysis method of GNSS observations to study the impact of factors such as distance, angle, and feature accuracy on the random measurement error of LiDAR. The experimental results show that feature accuracy is the main factor affecting the measurement error in the LiDAR point cloud feature matching positioning model, compared with distance and angle factors, even under different sensor specifications, point cloud densities, prior maps, and urban road scenes. Furthermore, a simple random measurement error model based on the feature accuracy factor is used to verify the effect of parameter estimation, and the results show that the random error model can effectively reduce the error of pose parameter estimation, with an improvement effect of about 50%.https://www.mdpi.com/2072-4292/17/8/1457LiDAR matching positioningmain influencing factorrandom measurement errorfeature accuracyurban road scene |
| spellingShingle | Guoliang Liu Wang Gao Shuguo Pan Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching Positioning Remote Sensing LiDAR matching positioning main influencing factor random measurement error feature accuracy urban road scene |
| title | Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching Positioning |
| title_full | Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching Positioning |
| title_fullStr | Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching Positioning |
| title_full_unstemmed | Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching Positioning |
| title_short | Analysis of Factors Affecting Random Measurement Error in LiDAR Point Cloud Feature Matching Positioning |
| title_sort | analysis of factors affecting random measurement error in lidar point cloud feature matching positioning |
| topic | LiDAR matching positioning main influencing factor random measurement error feature accuracy urban road scene |
| url | https://www.mdpi.com/2072-4292/17/8/1457 |
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