Enhancing navigation performance through visual-inertial odometry in GNSS-degraded environment

Liao, Jianchi; Li, Xingxing; Wang, Xuanbin; Li, Shengyu; Wang, Huidan

doi:10.1007/s10291-020-01056-0

Cited by 41 publications

(12 citation statements)

References 24 publications

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“…Furthermore, it was effective at solving the image-processing delay problem using the EKF recalculation. Visual inertial odometers are effective in maintaining the continuity of measurements when they are integrated with GPS, especially in tight coupling integration, as shown in the work presented in [ 22 ], where they obtained good results and maintained high continuity. However, GPS is subject to a provider even in the tight coupling mode.…”

Section: Discussionmentioning

confidence: 99%

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

Shahoud

Shashev

Shidlovskiy

2022

Sensors

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This paper presents the implementation of a mutual-aided navigation system for an aerial vehicle. Employing all available sensors in navigation is effective at maintaining continuous and optimal results. The images offer a lot of information about the surrounding environment, but image processing is time-consuming and causes timing problems. While traditional fusion algorithms tend to reduce the delay errors or ignore them, this research depends on state estimation recalculation during the delay time and on sequential filtering. To reduce the image matching time, the map is processed offline, then key point clusters are stored to avoid feature recalculation online. The sensors’ information is used to bound the search space for the matched features on the map, then they are reprojected on the captured images to exclude the unuseful part from processing. The suggested mutual-aided form compensates for the inertial system drift, which enhances the system’s accuracy and independence. The system was tested using data collected from a real flight using a DJI drone. The measurements from an inertial measurement unit (IMU), camera, barometer, and magnetometer were fused using a sequential Kalman Filter. The final results prove the efficiency of the suggested system to navigate with high independency, with an RMS position error of less than 3.5 m.

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Section: Discussionmentioning

confidence: 99%

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

Shahoud

Shashev

Shidlovskiy

2022

Sensors

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“…They are utilised for the measurement update. By the proposed CSCKF‐based VIG system, the state augmentation is no longer required to account for the past camera poses used in the conventional filter‐based VIO or VIG algorithms [11–14, 16, 25, 26]. For the reason, the computational burden is reduced significantly.…”

Section: Proposed Visual‐inertial‐gnss Systemmentioning

confidence: 99%

“…Ref. [25] utilises differential multi-GNSS observations and visual measurements from a stereo camera to augment INS. Ref.…”

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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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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“…GNSS and INS possess highly complementary characteristics, which can effectively overcome the adverse effects of the harsh environment with the two systems integrated [ 6 ]. Therefore, GNSS/INS integrated navigation is widely utilized in naturalistic driving, high-precision vehicle navigation, Intelligent Transportation system (ITS), and autonomous driving [ 7 , 8 , 9 , 10 ]. Based on the integrated method, GNSS/INS integrated navigation can be divided into loosely coupled, tightly coupled, and deeply coupled.…”

Section: Introductionmentioning

confidence: 99%

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

et al. 2022

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Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the K-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the K-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.

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Enhancing navigation performance through visual-inertial odometry in GNSS-degraded environment

Cited by 41 publications

References 24 publications

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

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

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

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