OSIRIS-REx OLA point cloud registration based on keypoints matching

Ji Feng, Rong Huang, Huan Xie, Yaqiong Wang, Xiangsui Zeng, Jie Chen, Yifan Wang, Hongji Ni
{"title":"OSIRIS-REx OLA point cloud registration based on keypoints matching","authors":"Ji Feng, Rong Huang, Huan Xie, Yaqiong Wang, Xiangsui Zeng, Jie Chen, Yifan Wang, Hongji Ni","doi":"10.1117/12.2665810","DOIUrl":null,"url":null,"abstract":"The OSIRIS-Rex Laser Altimeter (OLA) is the first scanning lidar instrument to fly a planetary mission. The OLA scans Bennu for about a month during the Orbit B mission phase and obtains 911 frames of point clouds. Due to the uncertainty of spacecraft position and pointing, there will be offsets between overlapping point clouds. In our method, the point cloud is first projected onto a plane, and then the keypoints are extracted using the SIFT algorithm. Finally, we perform coarse and global adjustments based on keypoints. However, low accuracy of the corresponding keypoints will lead to bad registration. In order to improve the accuracy of keypoints matching and point cloud registration, we use the tuple test and RANSAC algorithm to eliminate mismatched points. For the overlapping point clouds of two frames, the RMSE between keypoints is about 0.04m after registration. The results show that this method can improve the accuracy of point cloud registration to a certain extent and meet the application requirements.","PeriodicalId":258680,"journal":{"name":"Earth and Space From Infrared to Terahertz (ESIT 2022)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth and Space From Infrared to Terahertz (ESIT 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2665810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The OSIRIS-Rex Laser Altimeter (OLA) is the first scanning lidar instrument to fly a planetary mission. The OLA scans Bennu for about a month during the Orbit B mission phase and obtains 911 frames of point clouds. Due to the uncertainty of spacecraft position and pointing, there will be offsets between overlapping point clouds. In our method, the point cloud is first projected onto a plane, and then the keypoints are extracted using the SIFT algorithm. Finally, we perform coarse and global adjustments based on keypoints. However, low accuracy of the corresponding keypoints will lead to bad registration. In order to improve the accuracy of keypoints matching and point cloud registration, we use the tuple test and RANSAC algorithm to eliminate mismatched points. For the overlapping point clouds of two frames, the RMSE between keypoints is about 0.04m after registration. The results show that this method can improve the accuracy of point cloud registration to a certain extent and meet the application requirements.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于关键点匹配的OSIRIS-REx OLA点云配准
OSIRIS-Rex激光高度计(OLA)是第一个执行行星任务的扫描激光雷达仪器。在轨道B任务阶段,OLA对Bennu进行了大约一个月的扫描,获得了911帧的点云。由于航天器位置和指向的不确定性,重叠点云之间会产生偏移。该方法首先将点云投影到平面上,然后使用SIFT算法提取关键点。最后,根据关键点进行粗调整和全局调整。然而,相应的关键点精度低,会导致配准不良。为了提高关键点匹配和点云配准的精度,我们使用元组测试和RANSAC算法来消除不匹配点。对于两帧重叠的点云,配准后关键点之间的RMSE约为0.04m。结果表明,该方法能在一定程度上提高点云配准的精度,满足应用要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Simulation of SAGCM structure InGaAs/InP SPAD using COMSOL multiphysics Research on noise suppression of inter-satellite laser pointing jitter The instrumental responsivity effect to the calibrated radiances of infrared hyperspectral benchmark sounder Visualization of radiation intensity sequences for space infrared target recognition A space-time downscaling approach of Fengyun-4A satellite based on deep learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1