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http://hdl.handle.net/10397/87973
Title: | Robust visual-inertial integrated navigation system aided by online sensor model adaption for autonomous ground vehicles in urban areas | Authors: | Bai, X Wen, W Hsu, LT |
Issue Date: | 2020 | Source: | Remote sensing, 2020, v. 12, no. 10, 1686 | Abstract: | 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 Mestimator 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 Mestimator. 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. | Keywords: | Adaptive tuning Autonomous systems Dynamic objects Positioning Urban canyons Visual-inertial integrated navigation system (VINS) |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Remote sensing | EISSN: | 2072-4292 | DOI: | 10.3390/rs12101686 | Rights: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The following publication 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, is available at https://doi.org/10.3390/rs12101686 |
Appears in Collections: | Journal/Magazine Article |
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