Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92749
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Aeronautical and Aviation Engineering | en_US |
dc.contributor | Department of Electrical Engineering | en_US |
dc.creator | Kan, YC | en_US |
dc.creator | Hsu, LT | en_US |
dc.creator | Chung, E | en_US |
dc.date.accessioned | 2022-05-16T09:07:32Z | - |
dc.date.available | 2022-05-16T09:07:32Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/92749 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Kan, Y. C., Hsu, L. T., & Chung, E. (2021). Performance Evaluation on Map-based NDT Scan Matching Localization using Simulated Occlusion Datasets. IEEE Sensors Letters, 5(3), 5500204 is available at https://doi.org/10.1109/LSENS.2021.3060097 | en_US |
dc.subject | Autonomous driving | en_US |
dc.subject | LiDar | en_US |
dc.subject | Localization | en_US |
dc.subject | Normal distribution transform (NDT) scan matching | en_US |
dc.subject | Point cloud occlusion | en_US |
dc.subject | Sensor systems | en_US |
dc.title | Performance evaluation on map-based NDT scan matching localization using simulated occlusion datasets | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 5 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1109/LSENS.2021.3060097 | en_US |
dcterms.abstract | This letter presents a performance evaluation on the conventional normal distribution transform (NDT) map-based scan matching under the presence of occlusion. The LiDAR map-based localization method enables centimeter level accuracy positioning; however, the state-of-the-art algorithms do not achieve the same performance when excessive unexpected objects, such as pedestrians or dynamic vehicles, occlude the field of view of the LiDAR. Although the NDT scan matching is able to cope with slight geometrical change of environment, the presence of unexpected objects still induces matching error due to the discrepancy created between the real-time scan and the prebuild map. In this letter, we manually place bounding boxes into realistic medium-urban LiDAR scans to simulate occlusion scenarios and investigate the effect of the point cloud occlusion on the map-based NDT scan matching method performance. Under the occluded situations, the induced positioning error is found to be positively correlated to the change of heading angle. Significant 3-D localization errors peaks, up to 42.41 cm, are identified repeatedly at circumstances while the LiDAR encounters a substantial change of yaw angle, and these error peaks amplify as the occlusion rate increases. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE sensors letters, Mar. 2021, v. 5, no. 3, 5500204 | en_US |
dcterms.isPartOf | IEEE sensors letters | en_US |
dcterms.issued | 2021-03 | - |
dc.identifier.scopus | 2-s2.0-85101774343 | - |
dc.identifier.eissn | 2475-1472 | en_US |
dc.identifier.artn | 5500204 | en_US |
dc.description.validate | 202205 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AAE-0052 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Research Institute for Sustainable Urban Development, Hong Kong Polytechnic University; Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 46177383 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Hsu_Performance_Evaluation_Map-Based.pdf | Pre-Published version | 1.16 MB | Adobe PDF | View/Open |
Page views
49
Last Week
0
0
Last month
Citations as of May 19, 2024
Downloads
111
Citations as of May 19, 2024
SCOPUSTM
Citations
11
Citations as of May 16, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.