SINS/Odometer/Doppler Radar High-Precision Integrated Navigation Method for Land Vehicle

Bo, Yang; Xue, Liang; Fan, Hongdong; Yang, Xiaogang

doi:10.1109/jsen.2021.3071181

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…Most tunnel location methods use odometry or INS (Inertial Navigation System). For instance, YANG et al [11] introduced a precise navigation technique that combines an inertial navigation system, odometry, and Doppler radar. Schaer et al [12] proposed a method for error correction using control points in subway tunnels.…”

Section: Defect Localizationmentioning

confidence: 99%

A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

Gan,

Teng,

Chang

et al. 2024

Sustainability

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Existing tunnel defect detection methods often lack repeated inspections, limiting longitudinal analysis of defects. To address this, we propose a multi-information fusion approach for continuous defect monitoring. Initially, we utilized the You Only Look Once version 7 (Yolov7) network to identify defects in tunnel lining videos. Subsequently, defect localization is achieved with Super Visual Odometer (SuperVO) algorithm. Lastly, the SuperPoint–SuperGlue Matching Network (SpSg Network) is employed to analyze similarities among defect images. Combining the above information, the repeatability detection of the disease is realized. SuperVO was tested in tunnels of 159 m and 260 m, showcasing enhanced localization accuracy compared to traditional visual odometry methods, with errors measuring below 0.3 m on average and 0.8 m at maximum. The SpSg Network surpassed the depth-feature-based Siamese Network in image matching, achieving a precision of 96.61%, recall of 93.44%, and F1 score of 95%. These findings validate the effectiveness of this approach in the repetitive detection and monitoring of tunnel defects.

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Section: Defect Localizationmentioning

confidence: 99%

A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

Gan,

Teng,

Chang

et al. 2024

Sustainability

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“…Other possible aiding sensors measuring the vehicle velocity are Doppler radars. Yang et al (2021) investigate the use of a Doppler radar combined with a strapdown inertial navigation system and wheel odometry for land vehicle navigation. The authors thereby use the Doppler radar only to get the longitudinal velocity.…”

Section: Related Workmentioning

confidence: 99%

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Benz

Gehrt

Zweigel

et al. 2023

navi

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The automation of mining haulage vehicles has great potential in terms of safety and economy. The performance of autonomous vehicles depends largely on highly accurate vehicle state information. Deep mines are especially challenging, as satellite-based localization methods are reaching their limits. Therefore, we introduce a new navigation filter concept for the precise and robust localization of the haulage fleet that can handle temporary GNSS interruptions in deep open-pit mines. The multi-sensor navigation filter utilizes an inertial measurement unit and is aided by GNSS. We introduce a new optical speed sensor update within the tightly coupled unscented Kalman filter. The speed sensor measures the slip-free two-dimensional speed above ground. The filter was validated with an articulated dumper in a gravel pit. The new filter achieved a mean position error of 0.24 m during a test drive of 190 s with a simulated GNSS outage of 90 s.

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“…Therefore, monad sample algorithm can be adopted for the solution of Ф m , Ф m = ∆θ, ∆𝜽 is the angular increment from t m 1 time to t m time. After the Ф m is obtained, the attitude is updated by using Equation (12).…”

Section: Attitude Update Algorithm For Strapdown Inertial Navigationmentioning

confidence: 99%

“…The experimental results show that the system can effectively reduce the error to achieve effective positioning when the GPS is out of lock for a long time, but it only reduces the error for the longitudinal direction of the vehicle, the lateral positioning error of the vehicle is still large. Yang et al, proposed a high-precision integrated navigation method for land vehicles using SINS, odometer, and Doppler radar [12]. The experimental results of the method show that the positioning accuracy of the vehicle in the absence of a GPS signal of the vehicle compared to only the inertial guidance system has significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

Jiang

Liu

et al. 2022

Applied Sciences

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The satellite navigation signal in the tunnel is weak, and it is difficult to achieve accurate lateral positioning in complex conditions such as low-speed congestion by relying solely on inertial navigation or line image recognition, which is one of the problems of automatic driving at present. In this paper, a lane-level location method based on multi-sensor fusion is proposed. Using the machine vision method, detecting lane lines with the monocular camera, and fitting the lane lines to determine the driving status of the vehicle based on the lane line information. The top view of the lane line is taken by the binocular camera, and the distance of the vehicle from the lane line and the width of the lane are calculated from the pictures taken by the binocular camera. Obtaining the heading angle information of the vehicle using the gyroscope in inertial navigation and the distance information of the vehicle using the odometer. When a car changes lanes or overtakes, the new lane the vehicle is in is calculated by calculating the difference in heading angle and combining it with the lane width and odometer information so as to complete the lateral positioning of the vehicle. The simulation results show that the algorithm has high lateral positioning accuracy. The positioning accuracy is less affected by the drift of inertial elements, and the error will not accumulate.

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SINS/Odometer/Doppler Radar High-Precision Integrated Navigation Method for Land Vehicle

Cited by 17 publications

References 15 publications

A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

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