Cubature Kalman Filter With Both Adaptability and Robustness for Tightly-Coupled GNSS/INS Integration

Gao, Bingbing; Hu, Gaoge; Zhong, Yongmin; Zhu, Xinhe

doi:10.1109/jsen.2021.3073963

Cited by 82 publications

(32 citation statements)

References 36 publications

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“…As a result, for closed-error scheme integration, the INS can provide reliable positioning while experiencing short GNSS signal outages [4]. However, if the GNSS outage occurs for a prolonged period of time, the system will rely on the performance of the INS, which is prone to a significant drift, especially when a low-cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) is used [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments

Abdelaziz

El-Rabbany

2022

Sensors

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Traditional navigation systems rely on GNSS/inertial navigation system (INS) integration, in which the INS can provide reliable positioning during short GNSS outages. However, if the GNSS outage persists for prolonged periods of time, the performance of the system will be solely dependent on the INS, which can lead to a significant drift over time. As a result, the need to integrate additional onboard sensors is essential. This study proposes a robust loosely coupled (LC) integration between the INS and LiDAR simultaneous mapping and localization (SLAM) using an extended Kalman filter (EKF). The proposed integrated navigation system was tested for three different driving scenarios and environments using the raw KITTI dataset. The first scenario used the KITTI residential datasets, totaling 48 min, while the second case study considered the KITTI highway datasets, totaling 7 min. For both case studies, a complete absence of the GNSS signal was assumed for the whole trajectory of the vehicle in all drives. In contrast, the third case study considered the use of minimal assistance from GNSS, which mimics the intermittent receipt and loss of GNSS signals for different driving environments. The positioning results of the proposed INS/LiDAR SLAM integrated system outperformed the performance of the INS for the residential datasets with an average reduction in the root mean square error (RMSE) in the horizontal and up directions of 88% and 32%, respectively. For the highway datasets, the RMSE reductions were 70% and 0.2% for the horizontal and up directions, respectively.

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Section: Introductionmentioning

confidence: 99%

An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments

Abdelaziz

El-Rabbany

2022

Sensors

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“…Ref. [ 8 ] rigorously derives a novel adaptive CKF (Cubature Kalman filter) with fading memory for kinematic modelling errors and a new robust CKF with emerging memory for observation modelling errors, using the concept of Mahalanobis distance without involving artificial empiricism. However, CKF will discard part of the approximation error, which makes the filtering not meet the quasi consistency, so that it is unable to accurately estimate the true value of the state.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Fusion Attitude and Heading Measurement Method of MEMS/GNSS Based on Covariance Matching

Sun

2022

Micromachines

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Aimed at the problem of filter divergence caused by unknown noise statistical characteristics or variable noise characteristics in an MEMS/GNSS integrated navigation system in a dynamic environment, on the basis of revealing the parameter adjustment logic of covariance matching adaptive technology, a fusion adaptive filtering scheme combining innovation-based adaptive estimation (IAE) and the adaptive fading Kalman filter (AFKF) is proposed. By setting two system tuning parameters, for the process noise covariance adaptation loop and the measurement noise covariance adaptation loop, covariance matching is sped up and achieves an effective suppression of filter divergence. The vehicle-mounted experimental results show that the mean square error of the combined attitude error obtained based on the fusion filtering method proposed in this paper is better than 0.5°, and the mean square error of the heading error is better than 1.5°. The results can provide technical support for the continuous extraction of low-cost attitude information from mobile platforms.

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“…There are two major categories of loosely coupled algorithms: rule-based and data-driven approaches. For rule-based methods, filters are mostly applied, including Kalman filter [], Extended Kalman filter [9], Unscented Kalman filter [10], and particle filter [11]. Filtering algorithms require the knowledge of all measurement models and noise to perform GNSS/INS fusion.…”

Section: ‚ Introductionmentioning

confidence: 99%

Experimental Evaluation of GNSS and IMU Fusion Using Gated Recurrent Unit

Petrunin

Tsourdos

2022

2022 Integrated Communication, Navigation and Surveillance Conference (ICNS)

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In this paper, a data-driven Inertial navigation systems (INS) and Global Navigation Satellite System (GNSS) fusion algorithm based on the use of the Gated Recurrent Unit (GRU) is proposed. In this project, we trained the GRU neural network with Inertial Measurement Unit (IMU) raw data and GNSS Position, Velocity and Timing (PVT) solutions as input and the position difference between GNSS and ground truth as labels. Therefore, the trained model can estimate the rover's positions by subtracting the predicted GNSS error from GNSS positions given IMU raw measurements and GNSS PVT solutions. To evaluate the performance of GNSS/INS fusion algorithms in realistic scenarios, we developed an experimental platform. Our experimental platform consists of a moving test rig and an external validation system. The moving test rig consists of a rover equipped with an LPMS-CU2: 9-Axis Inertial Measurement Unit (IMU) and U-Blox ZED-F9P GNSS receiver. For validation purposes, we employ an onboard real-time kinematic positioning (RTK)-GNSS receiver. The test scenarios include both open-sky and challenging conditions near buildings, which is beneficial for devolving and testing urban navigation systems. After training with collected experimental data in multiple test scenarios, the proposed algorithm is able to improve GNSS positioning accuracy by more than 60% for the open-sky environment and 30% for the urban environment.

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Cubature Kalman Filter With Both Adaptability and Robustness for Tightly-Coupled GNSS/INS Integration

Cited by 82 publications

References 36 publications

An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments

An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments

An Adaptive Fusion Attitude and Heading Measurement Method of MEMS/GNSS Based on Covariance Matching

Experimental Evaluation of GNSS and IMU Fusion Using Gated Recurrent Unit

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