Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91611
Title: Degeneration-aware outlier mitigation for visual inertial integrated navigation system in urban canyons
Authors: Bai, X 
Wen, W 
Hsu, L 
Issue Date: 2021
Source: IEEE transactions on instrumentation and measurement, 2021, v. 70, 5019915, https://doi.org/10.1109/TIM.2021.3126010
Abstract: In this paper, we proposed a graduated non-convexity (GNC) aided outlier mitigation method for the improvement of the visual-inertial integrated navigation system (VINS) to face the challenge of dynamic environments with numerous unexpected outlier measurements. A GNC optical flow algorithm was proposed for the detection of the outliers of feature tracking in the front-end of VINS by iteratively estimating the optical flow and the optimal weightings of feature correspondences. Then the feature correspondences with small weightings were excluded. However, excessive outlier exclusion may cause insufficient constraints on the state, causing degeneration of VINS. To solve the problem, this paper proposed to detect the potential degeneration based on the degree of constraint in different directions of the pose estimation. Then the number of features being considered was intelligently adapted based on the degeneration level to improve the geometry constraint in the coming epochs. We evaluated the effectiveness of the proposed method by using two challenging datasets (including challenging night scenarios) collected in urban canyons of Hong Kong. The results show that the proposed method can effectively reject the potential outlier visual measurements, and alleviate the degeneration, leading to improved positioning performance in both evaluated datasets.
Keywords: Graduated nonconvexity (GNC)
Navigation
Optimization method
Outlier measurements
Urban canyons
Visual-inertial integrated navigation system (VINS)
Visual odometry
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on instrumentation and measurement 
ISSN: 0018-9456
EISSN: 1557-9662
DOI: 10.1109/TIM.2021.3126010
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