Vision coupled GPS/INS scheme for helicopter navigation

Kim, Jae-Hyung; Lyou, Joon; Kwak, HwyKuen

doi:10.1007/s12206-010-0104-2

Cited by 13 publications

(4 citation statements)

References 13 publications

(10 reference statements)

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“…This system can provide short-term and relatively accurate position information by using a VO/IMU when the GNSS signal is temporarily missing, and for this reason the GNSS/IMU/VIO has become a trend in current integrated navigation. The construction of a combined GNSS and VO/IMU is based on the loose coupling method [47] in which the VO and IMU are tightly coupled (the combined process can be found in [48,49]). The velocity information from the VO/IMU and the position information of GNSS are used as inputs [50], and the system positions are estimated via filtering [51].…”

Section: Mathematical Model Of Ci-rkf In Gnss/imu/vo Integrated Positmentioning

confidence: 99%

Robust Kalman Filtering Based on Chi-square Increment and Its Application

Chen

et al. 2020

Remote Sensing

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In Global Navigation Satellite System (GNSS) positioning, gross errors seriously limit the validity of Kalman filtering and make the final positioning solutions untrustworthy. Thus, the detection and correction of gross errors have become indispensable parts of Kalman filtering. Starting by defining an incremental Chi-square method of recursive least squares, this paper extends this definition to Kalman filtering to detect gross errors, explains its nature and its relation with the currently adopted Chi-square variables of Kalman filtering in model and data spaces, and compares them with the predictive residual statistics. Two robust Kalman filtering models based on an incremental Chi-square method (CI-RKF) were established, and the one with a better incremental Chi-square component was selected based on a static accuracy evaluation experiment. We applied the selected robust model to the GNSS positioning and the GNSS/inertial measurement unit (IMU) / visual odometry (VO) integrated navigation experiment in an occluded urban area at the East China Normal University. We compared the results for conventional Kalman filtering (CKF) with a robust Kalman filtering constructed using predictive residual statistics and an Institute of Geodesy and Geophysics (IGGШ) weight factor, abbreviated as “PRS-IGG-RKF”. The results show that the overall accuracy of CI-RKF in GNSS positioning was improved by 22.68%, 54.33%, and 72.45% in the static experiment, and 12.30%, 7.50%, and 16.15% in the kinematic experiment. The integrated navigation results indicate that the CI-RKF fusion method increased the system positioning accuracy by 66.73%, 59.59%, and 59.62% in one of the severe occlusion areas, and 42.04%, 59.04%, and 52.41% in the other.

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Section: Mathematical Model Of Ci-rkf In Gnss/imu/vo Integrated Positmentioning

confidence: 99%

Robust Kalman Filtering Based on Chi-square Increment and Its Application

Chen

et al. 2020

Remote Sensing

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“…The Gaussian covariance function ( [27]) is adopted here. Supposing the time difference from an interpolated epoch i to a control epoch n is t (t > 0), then the covariance between i and n is calculated as follows: (20) where δt is the correlation time and C 0 is a base value of covariance and is positive. The covariance along different directions is zero.…”

Section: Covariance Functionsmentioning

confidence: 99%

“…Thus, the maintenance of a high degree of accuracy in the MLS in all kinds of environment becomes a key problem in terms of their application. In the fields of automation and robotics, some researchers proposed simultaneous localization and mapping (SLAM)-aided GNSS/INS methods under GNSS-blockage conditions [18][19][20]. SLAM uses cameras or 3D laser scanners or rotating 2D laser scanners to calculate the trajectory of vehicles through four steps: data association, map matching, loop detection and global optimization.…”

Section: Introductionmentioning

confidence: 99%

A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems

Mao

Zhang

et al. 2015

Remote Sensing

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In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order Remote Sens. 2015, 7 7403 to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.

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“…Due to the respective shortcomings of INS and GNSS, the positioning and attitude of autonomous vehicles are estimated generally by fusing multi-sensor information from inertial navigation system (INS), GNSS and other sensors [2,3]. Due to environmental disturbance or carrier vibration, the sensors of autonomous vehicles are prone to more significant errors or even faults [4]. Under the influence of vehicle fluctuation, the error of the inertial navigation system will increase suddenly, and even calculation failure will occur.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement

et al. 2022

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Because of the resilient frame structure, the factor graph is often used in navigation systems to solve the sensor asynchrony problem and realize plug-and-play effectively in the navigation information fusion method. To improve the fault detection performance of resilient integrated navigation systems under complex interference environments, a fault detection method in factor graph navigation framework based on INS measurements and GNSS pseudo-range measurements is proposed in this paper. The proposed method can effectively locate the fault satellite pseudo-range information based on the Chi-square fault detection method. Due to the plug-and-play characteristic of the factor graph framework, this method can quickly isolate faults to improve navigation accuracy. Finally, the effect of the method is verified by simulation and experiment. Compared with the Chi-square fault detection method, positioning accuracy is improved by more than 40%.

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Vision coupled GPS/INS scheme for helicopter navigation

Cited by 13 publications

References 13 publications

Robust Kalman Filtering Based on Chi-square Increment and Its Application

Robust Kalman Filtering Based on Chi-square Increment and Its Application

A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems

Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement

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