中文版 | English
Title

Robust Data Association Against Detection Deficiency for Semantic SLAM

Author
Publication Years
2023
DOI
Source Title
ISSN
1558-3783
EISSN
1558-3783
VolumePPIssue:99Pages:1-13
Abstract
Robust and accurate object association is essential for precise 3D object landmark inference in semantic Simultaneous Localization and Mapping (SLAM), and yet remains challenging due to the detection deficiency caused by high miss detection rate, false alarm, occlusion and limited field-of-view, etc. The 2D location of an object is a crucial complementary cue to the appearance feature, especially in the case of associating objects across frames under large viewpoint changes. However, motion model or trajectory pattern based methods struggle to infer object motion reliably with a moving camera. In this paper, by exploiting the local projective warping consistency, a local homography based 2D motion inference method is proposed to sequentially estimate the object location along with uncertainty. By integrating the deep appearance feature and semantic information, an object association method, named HOA, which is robust to detection deficiency is proposed. Experimental evaluations suggest that the proposed motion prediction method is capable of maintaining a low cumulative error over a long duration, which enhances the object association performance in both accuracy and robustness. Note to Practitioners-This work aims to consistently associate 2D detection boxes corresponding to the same 3D object across images. In tasks of landmark-based navigation, collision avoidance, grasping and manipulation, objects in the task space are commonly simplified into 3D enveloping surfaces (e.g. cuboid or ellipsoid) by using 2D object detection boxes from multiple image views, and accurate data association is a prerequisite for precise enveloping surface reconstruction. This problem remains challenging considering the imperfect object detections, the appearance similarity of objects and the unpredictable trajectory of the moving camera. This work proposes a long-term reliable 2D location prediction algorithm that is capable of handling the complex motion of the target. Along with the appearance feature extracted by a retrain-free deep learning based model, this work proposes an object association method that can simultaneously deal with multiple objects with unknown object categories under the moving camera scenario.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
National Natural ScienceFoundation of China[62173096] ; Leading Talentsof Guangdong Province Program["2016LJ06G498","2019QN01X761"] ; Guangdong Province Special Fund for ModernAgricultural Industry Common Key Technology Research and DevelopmentInnovation Team[2019KJ129] ; Programfor Guangdong Yangfan Innovative and Entrepreneurial Teams[2017YT05G026]
WOS Research Area
Automation & Control Systems
WOS Subject
Automation & Control Systems
WOS Accession No
WOS:000915771100001
Publisher
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10011152
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/424558
DepartmentDepartment of Electrical and Electronic Engineering
Affiliation
1.Biomimetic and Intelligent Robotics Laboratory (BIRL), Guangdong University of Technology, Guangzhou, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
Recommended Citation
GB/T 7714
Xubin Lin,Jiahao Ruan,Yirui Yang,et al. Robust Data Association Against Detection Deficiency for Semantic SLAM[J]. IEEE Transactions on Automation Science and Engineering,2023,PP(99):1-13.
APA
Xubin Lin,Jiahao Ruan,Yirui Yang,Li He,Yisheng Guan,&Hong Zhang.(2023).Robust Data Association Against Detection Deficiency for Semantic SLAM.IEEE Transactions on Automation Science and Engineering,PP(99),1-13.
MLA
Xubin Lin,et al."Robust Data Association Against Detection Deficiency for Semantic SLAM".IEEE Transactions on Automation Science and Engineering PP.99(2023):1-13.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Xubin Lin]'s Articles
[Jiahao Ruan]'s Articles
[Yirui Yang]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Xubin Lin]'s Articles
[Jiahao Ruan]'s Articles
[Yirui Yang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xubin Lin]'s Articles
[Jiahao Ruan]'s Articles
[Yirui Yang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.