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- Publisher Website: 10.1109/TRO.2023.3323936
- Scopus: eid_2-s2.0-85174837768
- WOS: WOS:001141871000023
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Article: Occupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors
Title | Occupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors |
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Authors | |
Keywords | Computational efficiency Image resolution Laser radar LiDAR Perception Memory management occupancy mapping Octrees range sensing Robot sensing systems Sensors |
Issue Date | 16-Oct-2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Transactions on Robotics, 2023 How to Cite? |
Abstract | Occupancy mapping is a fundamental component of robotic systems to reason about the unknown and known regions of the environment. This article presents an efficient occupancy mapping framework for high-resolution LiDAR sensors, termed D-Map. The framework introduces three main novelties to address the computational efficiency challenges of occupancy mapping. Firstly, we use a depth image to determine the occupancy state of regions instead of the traditional ray-casting method. Secondly, we introduce an efficient on-tree update strategy on a tree-based map structure. These two techniques avoid redundant visits to small cells, significantly reducing the number of cells to be updated. Thirdly, we remove known cells from the map at each update by leveraging the low false alarm rate of LiDAR sensors. This approach not only enhances our framework's update efficiency by reducing map size but also endows it with an interesting decremental property, which we have named D-Map. To support our design, we provide theoretical analyses of the accuracy of the depth image projection and time complexity of occupancy updates. Furthermore, we conduct extensive benchmark experiments on various LiDAR sensors in both public and private datasets. Our framework demonstrates superior efficiency in comparison with other state-of-the-art methods while maintaining comparable mapping accuracy and high memory efficiency. We demonstrate two real-world applications of D-Map for real-time occupancy mapping on a handle device and an aerial platform carrying a high-resolution LiDAR. In addition, we open-source the implementation of D-Map on GitHub to benefit society: https://github.com/hku-mars/D-Map. |
Persistent Identifier | http://hdl.handle.net/10722/339353 |
ISSN | 2023 Impact Factor: 9.4 2023 SCImago Journal Rankings: 3.669 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cai, Yixi | - |
dc.contributor.author | Kong, Fanze | - |
dc.contributor.author | Ren, Yunfan | - |
dc.contributor.author | Zhu, Fangcheng | - |
dc.contributor.author | Lin, Jiarong | - |
dc.contributor.author | Zhang, Fu | - |
dc.date.accessioned | 2024-03-11T10:35:56Z | - |
dc.date.available | 2024-03-11T10:35:56Z | - |
dc.date.issued | 2023-10-16 | - |
dc.identifier.citation | IEEE Transactions on Robotics, 2023 | - |
dc.identifier.issn | 1552-3098 | - |
dc.identifier.uri | http://hdl.handle.net/10722/339353 | - |
dc.description.abstract | <p>Occupancy mapping is a fundamental component of robotic systems to reason about the unknown and known regions of the environment. This article presents an efficient occupancy mapping framework for high-resolution LiDAR sensors, termed D-Map. The framework introduces three main novelties to address the computational efficiency challenges of occupancy mapping. Firstly, we use a depth image to determine the occupancy state of regions instead of the traditional ray-casting method. Secondly, we introduce an efficient on-tree update strategy on a tree-based map structure. These two techniques avoid redundant visits to small cells, significantly reducing the number of cells to be updated. Thirdly, we remove known cells from the map at each update by leveraging the low false alarm rate of LiDAR sensors. This approach not only enhances our framework's update efficiency by reducing map size but also endows it with an interesting decremental property, which we have named D-Map. To support our design, we provide theoretical analyses of the accuracy of the depth image projection and time complexity of occupancy updates. Furthermore, we conduct extensive benchmark experiments on various LiDAR sensors in both public and private datasets. Our framework demonstrates superior efficiency in comparison with other state-of-the-art methods while maintaining comparable mapping accuracy and high memory efficiency. We demonstrate two real-world applications of D-Map for real-time occupancy mapping on a handle device and an aerial platform carrying a high-resolution LiDAR. In addition, we open-source the implementation of D-Map on GitHub to benefit society: https://github.com/hku-mars/D-Map.</p> | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Transactions on Robotics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Computational efficiency | - |
dc.subject | Image resolution | - |
dc.subject | Laser radar | - |
dc.subject | LiDAR Perception | - |
dc.subject | Memory management | - |
dc.subject | occupancy mapping | - |
dc.subject | Octrees | - |
dc.subject | range sensing | - |
dc.subject | Robot sensing systems | - |
dc.subject | Sensors | - |
dc.title | Occupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TRO.2023.3323936 | - |
dc.identifier.scopus | eid_2-s2.0-85174837768 | - |
dc.identifier.eissn | 1941-0468 | - |
dc.identifier.isi | WOS:001141871000023 | - |
dc.identifier.issnl | 1552-3098 | - |