版权说明 操作指南
首页 > 成果 > 详情

EFRNet-VL: An end-to-end feature refinement network for monocular visual localization in dynamic environments

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Wang, Jingwen;Yu, Hongshan*;Lin, Xuefei;Li, Zechuan;Sun, Wei;...
通讯作者:
Yu, Hongshan;Lin, XF
作者机构:
[Sun, Wei; Lin, XF; Yu, Hongshan; Lin, Xuefei; Wang, Jingwen; Li, Zechuan; Yu, HS] Hunan Univ, Quanzhou Inst Ind Design & Machine Intelligence In, Coll Elect & Informat Engn, Sch Design, Changsha 410082, Hunan, Peoples R China.
[Lin, XF; Lin, Xuefei] Hunan Agr Univ, Coll Landscape Architecture & Art Design, Changsha 410128, Hunan, Peoples R China.
[Akhtar, Naveed] Univ Melbourne, Comp Sci & Software Engn, Melbourne, Vic 3010, Australia.
通讯机构:
[Lin, XF ; Yu, HS] H
Hunan Univ, Quanzhou Inst Ind Design & Machine Intelligence In, Coll Elect & Informat Engn, Sch Design, Changsha 410082, Hunan, Peoples R China.
Hunan Agr Univ, Coll Landscape Architecture & Art Design, Changsha 410128, Hunan, Peoples R China.
语种:
英文
关键词:
Visual localization;Pose estimation;Dynamic objects;Learning-based localization
期刊:
Expert Systems with Applications
ISSN:
0957-4174
年:
2024
卷:
243
页码:
122755
基金类别:
CRediT authorship contribution statement Jingwen Wang: Conceptualization, Methodology, Software, Investigation, Formal analysis, Writing – original draft, Writing – review & editing. Hongshan Yu: Conceptualization, Supervision, Resources, acquisition, Writing – review & editing, Methodology. Xuefei Lin: Software, Visualization, Investigation. Zechuan Li: Validation, Data curation. Wei Sun: Project administration, Supervision. Naveed Akhtar: Methodology, Writing – original draft, Writing – review & editing.
机构署名:
本校为通讯机构
摘要:
This study addresses the challenge of visual localization using monocular images, a crucial technology for autonomous systems that facilitates their navigation and interaction capabilities. With the advent of deep learning, visual localization techniques that utilize these methods have demonstrated improved robustness across diverse environments. Existing end-to-end models apply convolutional neural networks (CNNs) to extract salient features and directly estimate continuous spatial poses from map models that allow for implicit differentiation. Nonetheless, these models often falter in adaptin...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com