Observability analysis of inertial navigation errors from optical flow subspace constraint

Waldmann, Jacques; Silva, Raul Ikeda Gomes da; Chagas, Ronan Arraes Jardim

doi:10.1016/j.ins.2015.08.017

Cited by 14 publications

(9 citation statements)

References 44 publications

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“…And it is exceptive for the quaternion error, which was defined by the 3 × 1 angleerror vector δθ, as δq ≜ ½ 1 2 δθ T 1 T . Then, the linearized first-order approximation of continuous-time error-state kinematics 14,20,24…”

Section: System State and Propagationmentioning

confidence: 99%

“…Considering that the observed error has been injected into the updated nominal state, i.e.,x þ k 1 x − k þ Δx k so that δx þ k ¼ δx − k − Δx k , we defined an error reset function δx ≜ gðδxÞ ¼ δx − k − Δx k , where the reset operation was to ensure the error would reset δx←0, meanwhile, the covariance of error needed to be modified as well, 14 thus leading to E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 4 2 ; 3 2 6 ; 1 4 9…”

Section: Error-state Kalman Filter Resetmentioning

confidence: 99%

“…7 The observability analysis of INS errors for explicit measurements of a subspace constraint-based optical flow method was investigated in Jacques Waldmann's work. 14 These approaches require sufficient numbers of optical flow features to obtain the corresponding linear equations for the unknowns. Xu et al 2 presented an autonomous landing method using planar terrain features tracked in sequential images.…”

Section: Introductionmentioning

confidence: 99%

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Measurement model and observability analysis for optical flow-aided inertial navigation

Deng

Zhao

et al. 2019

Opt. Eng.

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We introduce a navigation filter fused with information of visual optical flow and data collected from an inertial measurement unit during GPS signal degradation. Under the assumption that the tracked feature points are located on a level plane, the feature depth can be explicitly expressed and an exact measurement model was derived. Moreover, an error model analysis for a block-matching-based optical flow algorithm has been investigated. The measurement error follows a Gaussian distribution, which is a prerequisite for leveraging the errorstate Kalman filter. Subsequently, a local observability analysis of the proposed filter was performed yielding the expression of three unobservable directions. We emphasize the ability of the proposed filter to estimate the aircraft's state, especially for accurate altitude estimation, without any help of prior knowledge or extra sensors. Finally, an extensive Monte Carlo analysis was used to verify the findings in the observability results showing that all states can be estimated except the absolute horizontal positions and rotation around the gravity vector. The effectiveness of the proposed filter is demonstrated through experimental hardware used to acquire outdoor flight test data.

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Section: System State and Propagationmentioning

confidence: 99%

Section: Error-state Kalman Filter Resetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measurement model and observability analysis for optical flow-aided inertial navigation

Deng

Zhao

et al. 2019

Opt. Eng.

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“…Besides, the calibration filter is established to estimate the lever-arm errors by employing the designed multiple rotation schemes. And the observability analysis for the designed multiple rotation schemes is conducted based on the PWCS method and the SVD method [22]- [24]. Ultimately, the multiple asynchronous rotation schemes are tested and compared as well as optimized by simulations and experiments.…”

Section: Introductionmentioning

confidence: 99%

The Asynchronous Gimbal-Rotation-Based Calibration Method for Lever-Arm Errors of Two Rotational Inertial Navigation Systems

Liu

2019

IEEE Access

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A rotational inertial navigation system (RINS) can enhance the navigation accuracy by modulating the constant inertial device biases with gimbals rotation. To further improve the navigation performance in marine applications, two or more RINSs are usually equipped in the same vessel to achieve the information fusion. However, the lever-arm effect caused by the outer and inner lever-arm errors of two RINSs would decrease the fusion accuracy of velocity and position information. To solve this problem, the asynchronous gimbal-rotation-based calibration method for the outer and inner lever-arm errors of two RINSs is proposed in this paper. The lever-arm error and navigation error models were seriously deduced. Based on system models, the design principles of rotation schemes of two RINSs were analyzed and summarized. The calibration filter with the velocity difference of two RINSs as the mere measurement was constructed. The multiple asynchronous rotation schemes according to the design principles were tested and compared as well as optimized by simulations and experiments. The results indicated that the outer and inner lever-arm errors could be well calibrated and the calibration accuracy could be improved with the optimal rotation schemes. The velocity differences of two RINSs could be significantly restrained, and the fusion accuracy of linear motion information could be obviously improved after the compensation of lever-arm errors, which demonstrated the effectiveness of the proposed calibration method. INDEX TERMS Outer and inner lever-arm errors, calibration method, design principles, asynchronous rotation scheme, rotational inertial navigation system.

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“…Cameras are cheap, light, and have low power requirements when compared to other sensors for localization such as laser scanners. 1,2 This makes them attractive sensors for low-cost situations or for applications where size and weight need to be tightly controlled, such as for micro aerial vehicles in a global navigation satellite system (GNSS)-denied environment. 3 In addition, cameras are also passive sensors, unlike sonar, radar, and laser scanners, which makes them useful in surveillance applications as they are difficult to detect and do not interfere with the environment they are observing.…”

Section: Introductionmentioning

confidence: 99%

Sensor to sensor calibration of the integrated INS/vision navigation system

Yang

Sun

Liu

et al. 2017

International Journal of Advanced Robotic Systems

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Due to the essential limitation of optical sensors, its integration with traditional Inertial Navigation System (INS) tends to be the focus in indoor navigation applications. In a low-cost INS/vision integrated navigation system, the relative position and gestures between slave systems are important coefficients. In order to solve the initial bias estimation problems involved in INS/vision integrated system, this article proposes a novel alignment approach based on the time-domain constraints. At first, on the basis of traditional initial alignment model, the time-domain constrained model is deduced, in which the time-related states and measurements are all modeled. In order to verify the advantage of state evaluability, both the traditional alignment model and the corresponding time-domain constrained model are analyzed via the nonlinear observability analysis method. At the end of this article, two groups of numerical simulations are implemented, and the corresponding results validate the effectiveness of the proposed time-domain constrained model.

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Observability analysis of inertial navigation errors from optical flow subspace constraint

Cited by 14 publications

References 44 publications

Measurement model and observability analysis for optical flow-aided inertial navigation

Measurement model and observability analysis for optical flow-aided inertial navigation

The Asynchronous Gimbal-Rotation-Based Calibration Method for Lever-Arm Errors of Two Rotational Inertial Navigation Systems

Sensor to sensor calibration of the integrated INS/vision navigation system

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