A low-cost integrated navigation system based on factor graph nonlinear optimization for autonomous flight

Taghizadeh, Sina; Nezhadshahbodaghi, M.; Safabakhsh, Reza

doi:10.1007/s10291-022-01265-9

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…The most popular estimator for the INS/GNSS integration is the Kalman filter (KF), which is widely used in many designs. Although much better performing factor graph optimisation (FGO) methods are available, their computationally extensive nature restricts their usage in embedded systems (Taghizadeh et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Low-cost integrated INS/GNSS using adaptive H∞ Cubature Kalman Filter

Taghizadeh

Safabakhsh

2023

J. Navigation

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We proposed an adaptive H-infinity Cubature Kalman Filter (AH∞CKF) to improve the navigation accuracy of a highly manoeuvrable unmanned aerial vehicle (UAV). AH∞CKF fuses the Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) measurements. Traditional state estimation filters like extended Kalman filters (EKF) and cubature Kalman filters (CKF) assume Gaussian noises. However, their performance degrades for non-Gaussian noises and system uncertainties encountered in real-world applications. Thus, designing filters robust to noise and distribution is crucial. AH∞CKF combines H∞CKF design with an added adaptive factor to adjust the state estimation covariance matrix according to measurements by exploiting the square root method to yield more numerically stable results (SrAH∞CKF). We conducted multiple dynamically rich flight tests to validate our claims using a UAV equipped with a commercially well-known GNSS solution. Results show that the SrAH∞CKF state estimation outperforms EKF and CKF methods on average by 90% in various statistical measures.

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Section: Introductionmentioning

confidence: 99%

Low-cost integrated INS/GNSS using adaptive H∞ Cubature Kalman Filter

Taghizadeh

Safabakhsh

2023

J. Navigation

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A distributed factor graph model solving method for cooperative localization of UAV swarms

Yang,

Ye,

Shao

et al. 2024

Meas. Sci. Technol.

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Accurate position information is crucial for unmanned aerial vehicles (UAV) to execute tasks. To balance the contradiction between the payload and localization accuracy of rotary UAVs, a cooperative localization method for UAV swarms based on the factor graph model is studied in this paper. Each UAV is equipped with a local factor graph model. A distributed factor graph model-solving method, AGA-Gauss–Newton conjugate gradient (GNCG), which combines an adaptive genetic algorithm and an improved GNCG algorithm, is proposed. The issue of falling into local optimal solutions was addressed by configuring the crossover and mutation behaviors of the genetic algorithm into an adaptive mode. The Gauss–Newton algorithm (GNQR) was improved using a conjugate gradient iteration, which effectively reduced the operation time of the algorithm. The simulation results indicate that the AGA-GNCG algorithm improves the localization accuracy with respect to the East–North–Up (ENU) frame by 58.8%, 60.6%, and 57.4% relative to the GNQR. Moreover, as the number of UAVs increases, the improved GNCG algorithm exhibits a significant improvement in computational efficiency compared to that of the GNQR algorithm.

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