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SIMSF: A Scale Insensitive Multi-Sensor Fusion Framework for Unmanned Aerial Vehicles Based on Graph Optimization
Dai B(代波)1,2,3; He YQ(何玉庆)1,2; Yang LY(杨丽英)1,2; Su Y(苏贇)1,2,3; Yue, Yufeng4; Xu WL(徐卫良)5
Department机器人学研究室
Corresponding AuthorHe, Yuqing(heyuqing@sia.cn)
Source PublicationIEEE ACCESS
ISSN2169-3536
2020
Volume8Pages:118273-118284
Indexed BySCI
WOS IDWOS:000549128700001
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China [2017YFC0822201] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [91648204, U1608253, 41412040202]
KeywordGlobal Positioning System Optimization State estimation Cameras Robustness Sensor fusion Multi-sensor fusion graph optimization fusion framework scale insensitive unmanned aerial vehicle state estimation
Abstract

Given the payload limitation of unmanned aerial vehicles (UAVs), lightweight sensors such as camera, inertial measurement unit (IMU), and GPS, are ideal onboard measurement devices. By fusing multiple sensors, accurate state estimations can be achieved. Robustness against sensor faults is also possible because of redundancy. However, scale estimation of visual systems (visual odometry or visual inertial odometry, VO/VIO) suffers from sensor noise and special-case movements such as uniform linear motion. Thus, in this paper, a scale insensitive multi-sensor fusion (SIMSF) framework based on graph optimization is proposed. This framework combines the local estimation of the VO/VIO and global sensors to infer the accurate global state estimation of UAVs in real time. A similarity transformation between the local frame of the VO/VIO and the global frame is estimated by optimizing the poses of the most recent UAV states. In particular, for VO, an initial scale is estimated by aligning the VO with the IMU and GPS measurements. Moreover, a fault detection method for VO/VIO is also proposed to enhance the robustness of the fusion framework. The proposed methods are tested on a UAV platform and evaluated in several challenging environments. A comparison between our results and the results from other state-of-the-art algorithms demonstrate the superior accuracy, robustness, and real-time performance of our system. Our work is also a general fusion framework, which can be extended to other platforms as well.

Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordROBUST
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational Key Research and Development Program of China[2017YFC0822201] ; National Natural Science Foundation of China[91648204] ; National Natural Science Foundation of China[U1608253] ; National Natural Science Foundation of China[41412040202]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27373
Collection机器人学研究室
Corresponding AuthorHe YQ(何玉庆)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
5.Department of Mechanical Engineering, University of Auckland, Auckland 1010, New Zealand
Recommended Citation
GB/T 7714
Dai B,He YQ,Yang LY,et al. SIMSF: A Scale Insensitive Multi-Sensor Fusion Framework for Unmanned Aerial Vehicles Based on Graph Optimization[J]. IEEE ACCESS,2020,8:118273-118284.
APA Dai B,He YQ,Yang LY,Su Y,Yue, Yufeng,&Xu WL.(2020).SIMSF: A Scale Insensitive Multi-Sensor Fusion Framework for Unmanned Aerial Vehicles Based on Graph Optimization.IEEE ACCESS,8,118273-118284.
MLA Dai B,et al."SIMSF: A Scale Insensitive Multi-Sensor Fusion Framework for Unmanned Aerial Vehicles Based on Graph Optimization".IEEE ACCESS 8(2020):118273-118284.
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