Low-cost and high performance ultra-tightly coupled GPS/INS integrated navigation method

Oh, Sang Heon; Hwang, Dong-Hwan

doi:10.1016/j.asr.2017.06.007

Cited by 25 publications

(10 citation statements)

References 12 publications

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“…Inertial measurement units (IMU) measure vehicle accelerations and turn rates that can be combined with the GNSS data to improve the trajectory estimation. This has been demonstrated in several studies [12][13][14][15][16][17]. Depending on the processed GNSS data, different variants can be distinguished.…”

Section: Introductionmentioning

confidence: 86%

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

et al. 2020

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Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.

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Section: Introductionmentioning

confidence: 86%

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

et al. 2020

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“…Equation (31) is the discrete receiver clock residual error model for the ultra‐tight integration navigation filter. Although it has the same form as the general receiver clock error model widely used in the tight integration navigation filter, such as the one presented in [23], their representations are quite different.…”

Section: Integration Navigation Filtermentioning

confidence: 99%

“…In order to ensure that the deviations between incoming GNSS signals and local replicas of them will not cause such unexpected excess, it is necessary to compensate the receiver clock errors in the NCO control generation of ultra‐tight integration. In consequence, the receiver clock model for the ultra‐tight integration navigation filter will be markedly different from the general receiver clock model, such as the one presented in [23], adopted in the tight integration navigation filter.…”

Section: Introductionmentioning

confidence: 99%

GNSS/MEMS IMU ultra‐tightly integrated navigation system based on dual‐loop NCO control method and cascaded channel filters

Liu

Zhan

Liu

2018

IET Radar, Sonar & Navigation

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“…The positioning estimation is done through different methods, as seen, for instance, in [30]- [34], where is shown the fusion of GPS and INS to bridge the period of GPS outages for vehicular navigation. The authors in [35] use random forest regression to enhance the positioning estimation of an airplane, whereas [40] employs Kalman filtering to a high-performance ultra-tightly coupled GPS/INS. In [36]- [39] is seen the use of adaptive networks to perform the Multisensor Data Fusion.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Paulino

Guimarães

Shiguemori

2019

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Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) -the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms -that have been shown able to improve considerably the accuracy of current positioning estimation systems for real-time UAV autonomous navigation, reducing the error in approximately 300cm 2 . In addition, the proposed methodology outperformed two other Computational Intelligence methodologies -Artificial Neural Networks and Regression Models, with 18 different tested approaches -in estimating an UAV position considering the Root-Mean-Square Error against the real trajectory. The generated Fuzzy Inference System has proved to be effective in providing an improved positioning estimation with a low computational burden, about 600 times faster than the fastest embedded sensor refresh rate.

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Low-cost and high performance ultra-tightly coupled GPS/INS integrated navigation method

Cited by 25 publications

References 12 publications

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

GNSS/MEMS IMU ultra‐tightly integrated navigation system based on dual‐loop NCO control method and cascaded channel filters

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

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