File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Occupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors

TitleOccupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors
Authors
KeywordsComputational efficiency
Image resolution
Laser radar
LiDAR Perception
Memory management
occupancy mapping
Octrees
range sensing
Robot sensing systems
Sensors
Issue Date16-Oct-2023
PublisherInstitute 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 Identifierhttp://hdl.handle.net/10722/339353
ISSN
2023 Impact Factor: 9.4
2023 SCImago Journal Rankings: 3.669
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCai, Yixi-
dc.contributor.authorKong, Fanze-
dc.contributor.authorRen, Yunfan-
dc.contributor.authorZhu, Fangcheng-
dc.contributor.authorLin, Jiarong-
dc.contributor.authorZhang, Fu-
dc.date.accessioned2024-03-11T10:35:56Z-
dc.date.available2024-03-11T10:35:56Z-
dc.date.issued2023-10-16-
dc.identifier.citationIEEE Transactions on Robotics, 2023-
dc.identifier.issn1552-3098-
dc.identifier.urihttp://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.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Transactions on Robotics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectComputational efficiency-
dc.subjectImage resolution-
dc.subjectLaser radar-
dc.subjectLiDAR Perception-
dc.subjectMemory management-
dc.subjectoccupancy mapping-
dc.subjectOctrees-
dc.subjectrange sensing-
dc.subjectRobot sensing systems-
dc.subjectSensors-
dc.titleOccupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors-
dc.typeArticle-
dc.identifier.doi10.1109/TRO.2023.3323936-
dc.identifier.scopuseid_2-s2.0-85174837768-
dc.identifier.eissn1941-0468-
dc.identifier.isiWOS:001141871000023-
dc.identifier.issnl1552-3098-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats