Attitude-Induced error modeling and compensation with GRU networks for the polarization compass during UAV orientation

Zhao, Deli; Liu, Yueze; Wu, Xindong; Hui, Dong; Wang, Chenguang; Tang, Jun; Shen, Chong; Li, Jun

doi:10.1016/j.measurement.2022.110734

Cited by 22 publications

(17 citation statements)

References 28 publications

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“…A polarized camera (based on Sony sensor IMX250MZR, 2448 × 2048 pixels, 24 fps, such as mentioned in Section 2.2.5 ) was mounted on board a 15 kg six-rotor UAV [ 211 ]. Polarimetric images were processed by gated recurrent unit (GRU) neural network generating an output refresh signal of 10 Hz with a heading accuracy of 0.5°.…”

Section: Polarized Vision For Robotics Navigationmentioning

confidence: 99%

Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

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Section: Polarized Vision For Robotics Navigationmentioning

confidence: 99%

Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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“…Heading errors are increased when the body axis tilt and the solar meridian angle are coupled. Utilizing a GRU neural network, an extensive investigation of attitude angle factors has been performed, creating a brand-new heading error modeling and compensating technique in [109]. In terms of forecasting UAV direction, their solution fared better than cutting-edge algorithms.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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Artificial Intelligence(AI) is an emerging technology that finds its application in various industries. Integration of AI in Unmanned Aerial Vehicles(UAVs) can lead to tremendous growth in the field of UAVs by improving flight safety and efficiency. Machine learning algorithms can enable UAVs to make real-time decisions in complex environments and reach the optimal solution that aims to fulfill a mission's requirements within the hardware constraints such as battery and payload. Several recent works in UAVs employed a variety of machine learning algorithms to enhance the capabilities of UAVs and assist them. Although several reviews have been published examining the various aspects of AI for UAVs, they are all pertaining to particular applications or technologies. Addressing this research gap, we present a comprehensive and diversified review to enable researchers to analyze the current and future requirements and develop the latest solutions utilizing AI. We have classified the reviewed works based on three different classification schemes: 1) application scenario-based, 2) AI algorithm-based, and 3) AI training paradigm-based. We have also presented a compilation of frameworks, tools, and libraries used in AI-integrated UAV systems. We identified that the integration of AI in UAVs has a wide array of applications ranging from path planning to resource allocation. We have observed that Reinforcement Learning based algorithms are more often used in AI-integrated UAV systems than other AI algorithms. Further, our findings reveal that UAV frameworks employing federated learning and other distributed machine learning paradigms are quickly emerging. Furthermore, we also have put forth several challenges and potential applications of AI-integrated UAV systems.

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“…The third category is by constructing a sensor model or fusing multiple algorithms, Liu [16] proposed a UAV attitude estimation algorithm considering motion acceleration interference, which improved the accuracy and interference resistance of UAV navigation system attitude estimation in dynamic environment. Zhao [17] proposed a gated recurrent unit (GRU) neural network-based method for modeling and compensating UAV heading errors, which has a good performance in predicting vehicle orientation. Liu [18] proposed an attitude solution algorithm based on acceleration correction model, which can attenuate the interference of non-gravitational acceleration on attitude calculation and avoid attitude angle divergence of aircraft navigation system in dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Research on Complementary Filtered Attitude Solution Method for Quadcopter Based on Double Filter Preprocessing

ZHANG,

ZHOU,

LIANG

et al. 2024

mech

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In the attitude calculation process of quadrotor, the traditional pre-filtering method has degraded the attitude solving accuracy due to incomplete denoising of accelerometer and gyroscope measurements. Therefore, a complementary filtered attitude solution method based on double filter pre-processing is proposed to address this problem. First, the signal decomposition, hard threshold denoising, and signal reconstruction of the accelerometer and gyroscope acquired data are performed using the Haar real-time wavelet filtering algorithm. The reconstructed signal is then filtered by the infinite impulse response ( IIR ) low-pass filtering algorithm to remove the residual high-frequency noise and complete the dual filtering preprocessing. Next, the gyroscope data is corrected with accelerometer data according to the complementary filtering algorithm. Finally, the corrected data are used to solve the quaternion and thus the attitude angle by combining the Longacurta method. The results show that the dual filtering preprocessing method can further reduce the noise in the measurements of accelerometer and gyroscope. The attitude angle results calculated by the proposed method in the static and dynamic hovering states of the four-rotor aircraft have a small degree of dispersion, which can effectively improve the accuracy of attitude calculation.

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Attitude-Induced error modeling and compensation with GRU networks for the polarization compass during UAV orientation

Cited by 22 publications

References 28 publications

Passive Polarized Vision for Autonomous Vehicles: A Review

Passive Polarized Vision for Autonomous Vehicles: A Review

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Research on Complementary Filtered Attitude Solution Method for Quadcopter Based on Double Filter Preprocessing

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