Graph-Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS-Degraded Environment

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%

“…Ref. [20,21] combines local VIO results with GNSS solutions. The above-mentioned loosely coupled VIG systems depend on the GNSS position solutions.…”

Section: Previous Research Studiesmentioning

confidence: 99%

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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“…Unlike previous loosely coupled works, tightly coupled method allows exploiting the correlations amongst all the measurements. In [23], an adaptive fusion GNSS and VIO system were proposed to achieve accurate global positioning. Similar to [23], [24] propose a methodology to tightly coupled fuse GNSS information with visual-inertial measurements in a nonlinear optimization estimator.…”

Section: Vins Combine With Gpsmentioning

confidence: 99%

A VINS Combined With Dynamic Object Detection for Autonomous Driving Vehicles

Huang

Wang

et al. 2022

IEEE Access

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“…If µ(t) exceeds the threshold, we dynamically adjust the RTK weight in the optimization phase. This process is shown in Equation (31), where γ(t) denotes the position covariance estimated by RTK; β(t) denotes the RTK weight in the optimization phase:…”

Section: The Improved Integration Strategy For Smartphonesmentioning

confidence: 99%

“…Generally, these algorithms are integrated with the robotic operating system (ROS) [30]. Nonetheless, VIO systems still undergo long-term drift [31]. In addition, VIO generates a pose output that includes the estimation of position and orientation in local coordinates, indicating that it is a relative positioning technique.…”

Section: Introductionmentioning

confidence: 99%

The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones

Niu

Guo

Shuai

et al. 2021

IJGI

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The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub.

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Graph-Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS-Degraded Environment

Cited by 39 publications

References 29 publications

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

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

A VINS Combined With Dynamic Object Detection for Autonomous Driving Vehicles

The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones

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