Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors

Wu, Allen D.; Johnson, Eric N.; Kaess, Michael; Dellaert, Frank; Chowdhary, Girish

doi:10.2514/1.i010023

Cited by 51 publications

(21 citation statements)

References 20 publications

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“…The use of quaternions is very common in these cases, because of the linearity of the quaternions, the avoidances of usage of trigonometric functions and the small number of parameters for successful implementation. The only disadvantage of the quaternions is that they don't have a simple geometric representation, so because of that they can't be measured directly [21]. While the problem with the measurement from the single camera is the lack of precise depth data, so it may be assumed that this distance is known and constant.…”

Section: Proposed Methods 31 Frame Setupmentioning

confidence: 99%

Attitude Determination of Unmanned Aerial Vehicle using Single Camera Vector Observations

Vladimirov¹,

Koceski²

2019

IJCA

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Technological development in the fields of electrical and mechanical engineering as well as computer and communication sciences in the last decade, have dramatically increased the popularity and fields of application of Unmanned Aerial Vehicles (UAVs). Despite the technological advancements, there are still very important challenges related to the operation of UAVs. One of the main challenging task for UAVs is to accurately determine their attitude during the flight, using the onboard sensors. This paper presents a framework for attitude determination of an UAV from single camera vector observations in a known environment. The framework has been experimentally evaluated. The results from the conducted evaluation suggest that the proposed method is appropriate and that it can be used in the control process.

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Section: Proposed Methods 31 Frame Setupmentioning

confidence: 99%

Attitude Determination of Unmanned Aerial Vehicle using Single Camera Vector Observations

Vladimirov¹,

Koceski²

2019

IJCA

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“…As outliers do not always arise (i.e., are rare), we reduce such computation cost if a test detects the time when outliers occur. All of the above methods were not validated for complicated systems such as unmanned aerial vehicles or vision-aided inertial navigation [6,22,23] or with sequential measurement updates [24,25].…”

Section: State Estimation For Measurements With Outliersmentioning

confidence: 99%

Robust Outlier-Adaptive Filtering for Vision-Aided Inertial Navigation

Lee

Johnson²

2020

Sensors

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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.

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“…In recent years, an increasing demand for the research of UAVs has prompted substantial interest in VIO systems [6][7][8][9]. Delmerico and Scaramuzza [10] provide a benchmark comparison of monocular VIO algorithms for flying robots.…”

Section: Visual-inertial Odometrymentioning

confidence: 99%

Latency Compensated Visual-Inertial Odometry for Agile Autonomous Flight

Lee

Johnson

2020

Sensors

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In visual-inertial odometry (VIO), inertial measurement unit (IMU) dead reckoning acts as the dynamic model for flight vehicles while camera vision extracts information about the surrounding environment and determines features or points of interest. With these sensors, the most widely used algorithm for estimating vehicle and feature states for VIO is an extended Kalman filter (EKF). The design of the standard EKF does not inherently allow for time offsets between the timestamps of the IMU and vision data. In fact, sensor-related delays that arise in various realistic conditions are at least partially unknown parameters. A lack of compensation for unknown parameters often leads to a serious impact on the accuracy of VIO systems and systems like them. To compensate for the uncertainties of the unknown time delays, this study incorporates parameter estimation into feature initialization and state estimation. Moreover, computing cross-covariance and estimating delays in online temporal calibration correct residual, Jacobian, and covariance. Results from flight dataset testing validate the improved accuracy of VIO employing latency compensated filtering frameworks. The insights and methods proposed here are ultimately useful in any estimation problem (e.g., multi-sensor fusion scenarios) where compensation for partially unknown time delays can enhance performance.

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Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors

Cited by 51 publications

References 20 publications

Attitude Determination of Unmanned Aerial Vehicle using Single Camera Vector Observations

Attitude Determination of Unmanned Aerial Vehicle using Single Camera Vector Observations

Robust Outlier-Adaptive Filtering for Vision-Aided Inertial Navigation

Latency Compensated Visual-Inertial Odometry for Agile Autonomous Flight

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