A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter
Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immedi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/6/1468 |
| _version_ | 1827625851043184640 |
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| author | Weiqi Wang Xiong You Lingyu Chen Jiangpeng Tian Fen Tang Lantian Zhang |
| author_facet | Weiqi Wang Xiong You Lingyu Chen Jiangpeng Tian Fen Tang Lantian Zhang |
| author_sort | Weiqi Wang |
| collection | DOAJ |
| description | Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immediately affects the ability of autonomous vehicles to accurately understand their environment and its expected impact. In recent years, researchers have improved the environmental perception capabilities of simultaneous localization and mapping (SLAM), object detection and tracking, semantic segmentation and panoptic segmentation, but relatively few studies have focused on enhancing environmental perception capabilities in adverse weather conditions, such as rain, snow and fog. To enhance the environmental perception of autonomous vehicles in adverse weather, we developed a dynamic filtering method called Dynamic Distance–Intensity Outlier Removal (DDIOR), which integrates the distance and intensity of points based on the systematic and accurate analysis of LiDAR point cloud data characteristics in snowy weather. Experiments on the publicly available WADS dataset (Winter Adverse Driving dataSet) showed that our method can efficiently remove snow noise while fully preserving the detailed features of the environment. |
| first_indexed | 2024-03-09T12:44:30Z |
| format | Article |
| id | doaj.art-07b4b5c6431f423f9da84008cbdbd3a9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:44:30Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-07b4b5c6431f423f9da84008cbdbd3a92023-11-30T22:13:28ZengMDPI AGRemote Sensing2072-42922022-03-01146146810.3390/rs14061468A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in WinterWeiqi Wang0Xiong You1Lingyu Chen2Jiangpeng Tian3Fen Tang4Lantian Zhang5Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, ChinaInstitute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, ChinaInstitute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, ChinaInstitute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, ChinaInstitute of Information and Communication, National University of Defense Technology, Wuhan 430014, ChinaBeijing Institute of Remote Sensing Information, Beijing 100011, ChinaAccurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immediately affects the ability of autonomous vehicles to accurately understand their environment and its expected impact. In recent years, researchers have improved the environmental perception capabilities of simultaneous localization and mapping (SLAM), object detection and tracking, semantic segmentation and panoptic segmentation, but relatively few studies have focused on enhancing environmental perception capabilities in adverse weather conditions, such as rain, snow and fog. To enhance the environmental perception of autonomous vehicles in adverse weather, we developed a dynamic filtering method called Dynamic Distance–Intensity Outlier Removal (DDIOR), which integrates the distance and intensity of points based on the systematic and accurate analysis of LiDAR point cloud data characteristics in snowy weather. Experiments on the publicly available WADS dataset (Winter Adverse Driving dataSet) showed that our method can efficiently remove snow noise while fully preserving the detailed features of the environment.https://www.mdpi.com/2072-4292/14/6/1468autonomous drivingde-snowing algorithmLiDAR point cloudsdata processing |
| spellingShingle | Weiqi Wang Xiong You Lingyu Chen Jiangpeng Tian Fen Tang Lantian Zhang A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter Remote Sensing autonomous driving de-snowing algorithm LiDAR point clouds data processing |
| title | A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter |
| title_full | A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter |
| title_fullStr | A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter |
| title_full_unstemmed | A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter |
| title_short | A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter |
| title_sort | scalable and accurate de snowing algorithm for lidar point clouds in winter |
| topic | autonomous driving de-snowing algorithm LiDAR point clouds data processing |
| url | https://www.mdpi.com/2072-4292/14/6/1468 |
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