State transformation extended Kalman filter–based tightly coupled strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter integration for seamless navigation of unmanned ground vehicle in urban areas

Du, Xueyu; Wang, Mao-song; Wu, Wenqi; Zhou, Peiyuan; Cui, Jiarui

doi:10.1177/17298806231158462

Cited by 3 publications

(6 citation statements)

References 24 publications

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“…For instance, GPSs may not provide accurate navigational data due to signal obstruction brought on by structures like buildings, bridges, and trees in urban areas, as well as multipath effects in tunnels and canyons. Long-term GPS rejection will cause the integrated system to deteriorate into a purely inertial navigation system, which will lead to the rapid accumulation of navigation errors [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

Cheng,

Wang,

et al. 2024

Entropy

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Combined SINS/GPS navigation systems have been widely used. However, when the traditional combined SINS/GPS navigation system travels between tall buildings, in the shade of trees, or through tunnels, the GPS encounters frequent signal blocking, which leads to the interruption of GPS signals, and as a result, the combined SINS/GPS-based navigation method degenerates into a pure inertial guidance system, which will lead to the accumulation of navigation errors. In this paper, an adaptive Kalman filtering algorithm based on polynomial fitting and a Taylor expansion is proposed. Through the navigation information output from the inertial guidance system, the polynomial interpolation method is used to construct the velocity equation and position equation of the carrier, and then the Taylor expansion is used to construct the virtual measurement at the moment of the GPS signal interruption, which can make up for the impact of the lack of measurement information on the combined SINS/GPS navigation system when the GPS signal is interrupted. The results of computer simulation experiments and road measurement tests based on the loosely combined SINS/GPS navigation system show that when the carrier faces a GPS signal interruption situation, compared with a combined SINS/GPS navigation algorithm that does not take any rescue measures, our proposed combined SINS/GPS navigation algorithm possesses a higher accuracy in the attitude angle estimation, a higher accuracy in the velocity estimation, and a higher accuracy in the positional localization, and the system possesses higher stability.

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Section: Introductionmentioning

confidence: 99%

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

Cheng,

Wang,

et al. 2024

Entropy

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“…Unlike GNSS, odometer (OD), laser Doppler velocimeter (LDV), camera, and LIDAR are fully autonomous sensors whose measurements are less affected by external interference and whose measurement errors do not accumulate over time. Therefore, in GNSS-denied environments, the introduction of OD, LDV, camera and LIDAR sensors can help suppress the divergence of UGV navigation errors [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the SINS/GNSS/LDV integration is likely to be more reliable than the traditional SINS/GNSS/OD integration in most cases because of the excellent performance of LDV. In a recent study, Du et al proposed an SINS/GNSS/LDV tightly coupled seamless navigation technique, and demonstrated its effectiveness and reliability [14]. However, what is used in this method is one-dimensional LDV (1D-LDV), which, like OD, can only provide the forward velocity of the vehicle, but not the upward velocity of the vehicle, and its three-dimensional (3D) velocity information is obtained by combining the well-known nonholonomic constraints (NHC) assumptions, which will not be able to constrain the upward error of the vehicle when the vehicle has large upward maneuvering during the period of GNSS failure effectively.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the method only uses a conventional centralized Kalman filter to fuse the information from the three sensors, and the conventional centralized Kalman filter has the following two main shortcomings: firstly, the conventional centralized Kalman filter cannot identify and isolate the abnormal information of the sensor in the navigation process, which will make the abnormal value of the sensor contaminate the integrated navigation result; and secondly, the conventional centralized Kalman filter cannot process the information of sensors with different frequencies well, which will inevitably lead to the waste of effective information of sensors. The above shortcomings in the literature [14] also exist in the existing SINS/GNSS/OD integrated navigation methods.…”

Section: Introductionmentioning

confidence: 99%

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A SINS/GNSS/2D-LDV integrated navigation scheme for unmanned ground vehicles

Xiang,

Zhang,

Wang

et al. 2023

Meas. Sci. Technol.

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In recent years, unmanned autonomous driving technology has attracted increasing attention from people, and become a research hotspot. Currently, the integration of strapdown inertial navigation system (SINS) and global navigation satellite systems (GNSSs) is the most common and effective navigation and positioning scheme for unmanned ground vehicles (UGVs) and unmanned aerial vehicles. However, this integrated system is unable to maintain a reliable positioning solution in challenging environments due to the inherent weakness of GNSS signals and the accumulation of SINS positioning errors over time. To address this issue, this paper proposes an integrated scheme based on an asynchronous Kalman filter for SINS, GNSS and two-dimensional (2D) laser Doppler velocimeter (LDV). In the proposed scheme, the SINS and 2D-LDV are tightly coupled to improve the robustness of the integrated system, and the error parameters between the 2D-LDV and the SINS are calibrated in real time during the validity of the GNSS signal. In addition, the designed asynchronous Kalman filter method evaluates the validity of the GNSS and 2D-LDV measurements in real time based on Mahalanobis distance of innovation vector and statistical property principle. Two groups of long-distance, high-mobility vehicle experiments conducted in challenging environments verify the validity of the proposed scheme. The experimental results show that the proposed SINS/GNSS/2D-LDV integrated navigation scheme has good environmental adaptability and reliability, and can maintain high horizontal and vertical positioning accuracy despite frequent GNSS signal failures, which can meet the needs of UGVs.

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“…Laser Doppler velocimetry (LDV) is a noninvasive technique for measuring the speed of moving solid-state surfaces or particle velocity in a fluid by laser interferometry [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Researchers continuously develop and design LDV devices suitable for different applications [15][16][17][18][19]. LDV devices with a dual-beam structure Sensors 2023, 23, 8411 2 of 10 have been widely used for fluid flow measurements for many years [20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Laser Velocimetry for the In Situ Sensing of Deep-Sea Hydrothermal Flow Velocity

Sun,

Zhang,

et al. 2023

Sensors

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Laser Doppler velocimetry (LDV) based on a differential laser Doppler system has been widely used in fluid mechanics to measure particle velocity. However, the two outgoing lights must intersect strictly at the measurement position. In cross-interface applications, due to interface effects, two beams of light become easily disjointed. To address the issue, we present a laser velocimeter in a coaxial arrangement consisting of the following components: a single-frequency laser (wavelength λ = 532 nm) and a Twyman–Green interferometer. In contrast to previous LDV systems, a laser velocimeter based on the Twyman–Green interferometer has the advantage of realizing cross-interface measurement. At the same time, the sensitive direction of the instrument can be changed according to the direction of the measured speed. We have developed a 4000 m level laser hydrothermal flow velocity measurement prototype suitable for deep-sea in situ measurement. The system underwent a withstand voltage test at the Qingdao Deep Sea Base, and the signal obtained was normal under a high pressure of 40 MPa. The velocity contrast measurement was carried out at the China Institute of Water Resources and Hydropower Research. The maximum relative error of the measurement was 8.82% when compared with the acoustic Doppler velocimeter at the low-speed range of 0.1–1 m/s. The maximum relative error of the measurement was 1.98% when compared with the nozzle standard velocity system at the high-speed range of 1–7 m/s. Finally, the prototype system was successfully evaluated in the shallow sea in Lingshui, Hainan, with it demonstrating great potential for the in situ measurement of fluid velocity at marine hydrothermal vents.

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State transformation extended Kalman filter–based tightly coupled strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter integration for seamless navigation of unmanned ground vehicle in urban areas

Cited by 3 publications

References 24 publications

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

A SINS/GNSS/2D-LDV integrated navigation scheme for unmanned ground vehicles

Laser Velocimetry for the In Situ Sensing of Deep-Sea Hydrothermal Flow Velocity

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