Position-Domain Hatch Filter for Integrated GPS/BeiDou/Altimeter

Kim, La Woo; Yoo, Won Jae

doi:10.33012/2019.16778

Cited by 1 publication

(4 citation statements)

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“…Similarly, all the GNSS measurements in (32) are utilised to compute the Kalman gain. The EKF measurement update is performed based on (21).…”

Section: Direct Measurement Update -Gnssmentioning

confidence: 99%

“…When utilising the carrier phase measurements, cycle slips and multipath errors need to be detected carefully. For this purpose, the Fault Detection Exclusion (FDE) algorithm [6,32] is applied. In this manner, the proposed CSCKF-based VIG system provides globally consistent and robust pose estimates even in the GNSSdegraded environments.…”

Section: Proposed Visual-inertial-gnss Systemmentioning

confidence: 99%

“…where all the components of the Z GNSS , H GNSS , and w GNSS matrices in (32) are presented in detail in Appendix D.…”

Section: Direct Measurement Update -Gnssmentioning

confidence: 99%

“…In(32), the stacked measurement vector and the observation matrix of the DD code and TD carrier phase is expressed as follows.…”

mentioning

confidence: 99%

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A tightly‐coupled compressed‐state constraint Kalman Filter for integrated visual‐inertial‐Global Navigation Satellite System navigation in GNSS‐Degraded environments

Lee

Kim

Lee

2022

IET Radar Sonar & Navi

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Efficient multi‐sensor fusion is crucial to provide accurate pose estimates for navigating various next‐generation autonomous vehicles such as self‐driving cars, personal air vehicles, urban air mobilities, and electronic vertical take‐off and landing aircraft with respect to a unified global reference frame. The integration of the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) provides globally uniform coordinate information, and the Visual‐Inertial Odometry (VIO) gives stable and accurate local pose estimates. However, the error of the integrated INS/GNSS increases rapidly in GNSS‐challenged environments, and the VIO system suffers from the long‐term error accumulation. To cope with these difficulties, this paper proposes a new tightly coupled Visual‐Inertial‐GNSS (VIG) system to achieve robust and drift‐free pose estimation utilising GNSS raw measurements, image features, and inertial measurements. A novel compressed state constraint Kalman Filter (CSCKF) is formulated to combine time‐propagated feature measurements and differential GNSS observations to aid INS. Compared to the conventional integrated navigation systems, the proposed CSCKF‐based VIG system is advantageous in maintaining the minimum number of states without unnecessary state augmentation and no loss of performance. The proposed system is evaluated by field experiments in different GNSS availability situations. The results show that accuracy is improved significantly in GNSS‐degraded environments compared to that of the conventional systems.

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“…Similarly, all the GNSS measurements in (32) are utilised to compute the Kalman gain. The EKF measurement update is performed based on (21).…”

Section: Direct Measurement Update -Gnssmentioning

confidence: 99%

Section: Proposed Visual-inertial-gnss Systemmentioning

confidence: 99%