Next Article in Journal
A Hybrid SAR/ISAR Approach for Refocusing Maritime Moving Targets with the GF-3 SAR Satellite
Next Article in Special Issue
Latency Compensated Visual-Inertial Odometry for Agile Autonomous Flight
Previous Article in Journal
Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition
Previous Article in Special Issue
Weak Knock Characteristic Extraction of a Two-Stroke Spark Ignition UAV Engine Burning RP-3 Kerosene Fuel Based on Intrinsic Modal Functions Energy Method
Open AccessArticle

Robust Outlier-Adaptive Filtering for Vision-Aided Inertial Navigation

by Kyuman Lee 1,*,† and Eric N. Johnson 2
1
School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Drive, Atlanta, GA 30313, USA
2
Faculty of Aerospace Engineering, The Pennsylvania State University, 229 Hammond Building, University Park, PA 16802, USA
*
Author to whom correspondence should be addressed.
Current address: 55 River Oaks Pl, San Jose, CA 95134, USA.
Sensors 2020, 20(7), 2036; https://doi.org/10.3390/s20072036
Received: 7 March 2020 / Revised: 28 March 2020 / Accepted: 1 April 2020 / Published: 4 April 2020
(This article belongs to the Special Issue UAV-Based Smart Sensor Systems and Applications)
With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surrounding environment and determines features or points of interest. With the extracted vision data and inertial measurement unit (IMU) dead reckoning, the most widely used algorithm for estimating vehicle and feature states in the back-end of V-INS is an extended Kalman filter (EKF). An important assumption of the EKF is Gaussian white noise. In fact, measurement outliers that arise in various realistic conditions are often non-Gaussian. A lack of compensation for unknown noise parameters often leads to a serious impact on the reliability and robustness of these navigation systems. To compensate for uncertainties of the outliers, we require modified versions of the estimator or the incorporation of other techniques into the filter. The main purpose of this paper is to develop accurate and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown outliers. Feature correspondence in image processing front-end rejects vision outliers, and then a statistic test in filtering back-end detects the remaining outliers of the vision data. For frequent outliers occurrence, variational approximation for Bayesian inference derives a way to compute the optimal noise precision matrices of the measurement outliers. The overall process of outlier removal and adaptation is referred to here as “outlier-adaptive filtering”. Even though almost all approaches of V-INS remove outliers by some method, few researchers have treated outlier adaptation in V-INS in much detail. Here, results from flight datasets validate the improved accuracy of V-INS employing the proposed outlier-adaptive filtering framework. View Full-Text
Keywords: V-INS; UAV; EKF; IMU; camera vision; computer vision; image processing; outlier rejection; adaptive filtering; sensor fusion; navigation V-INS; UAV; EKF; IMU; camera vision; computer vision; image processing; outlier rejection; adaptive filtering; sensor fusion; navigation
Show Figures

Figure 1

MDPI and ACS Style

Lee, K.; Johnson, E.N. Robust Outlier-Adaptive Filtering for Vision-Aided Inertial Navigation. Sensors 2020, 20, 2036.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop