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Article

Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

by 1, 2 and 1,*
1
Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
2
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1686; https://doi.org/10.3390/rs12101686
Received: 1 March 2020 / Revised: 20 May 2020 / Accepted: 20 May 2020 / Published: 25 May 2020
The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method. View Full-Text
Keywords: visual-inertial integrated navigation system (VINS); dynamic objects; adaptive tuning; positioning; autonomous systems; urban canyons visual-inertial integrated navigation system (VINS); dynamic objects; adaptive tuning; positioning; autonomous systems; urban canyons
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MDPI and ACS Style

Bai, X.; Wen, W.; Hsu, L.-T. Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas. Remote Sens. 2020, 12, 1686. https://doi.org/10.3390/rs12101686

AMA Style

Bai X, Wen W, Hsu L-T. Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas. Remote Sensing. 2020; 12(10):1686. https://doi.org/10.3390/rs12101686

Chicago/Turabian Style

Bai, Xiwei, Weisong Wen, and Li-Ta Hsu. 2020. "Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas" Remote Sensing 12, no. 10: 1686. https://doi.org/10.3390/rs12101686

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