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Title: SIMSF : a scale insensitive multi-sensor fusion framework for unmanned aerial vehicles based on graph optimization
Authors: Dai, Bo
He, Yuqing
Yang, Liying
Su, Yun
Yue, Yufeng
Xu, Weiliang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Dai, B., He, Y., Yang, L., Su, Y., Yue, Y., & Xu, W. (2020). SIMSF : a scale insensitive multi-sensor fusion framework for unmanned aerial vehicles based on graph optimization. IEEE Access, 8, 118273-118284. doi:10.1109/ACCESS.2020.3000555
Journal: IEEE Access
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.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3000555
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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