A Variable Proportion Adaptive Federal Kalman Filter for INS/ESGM/GPS/DVL Integrated Navigation System

Yuan, Gannan; Yuan, Kefei; Zhang, Hongwei

doi:10.1109/cso.2011.63

Cited by 15 publications

(10 citation statements)

References 2 publications

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“…GRID-based vision navigation can be integrated with GPS/IMU, where the GRID can serve as a new sensor for federal Kalman filtering (FKF) in navigation calculation [ 45 ]. Our paper merely discusses vision navigation during GPS loss while one or more GPS satellites remain.…”

Section: Experimental Section and Discussionmentioning

confidence: 99%

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

et al. 2016

Sensors

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In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.

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Section: Experimental Section and Discussionmentioning

confidence: 99%

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

et al. 2016

Sensors

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“…As shown in Fig 10, Federal Kalman filter is a decentralized filter usually applied for complicated systems. It can solve the challenge of estimating the state with high dimensional computation, good fault tolerance and flexible design [81].…”

Section: B Kalman Filteringmentioning

confidence: 99%

Toward Autonomous Driving in Arctic Areas

Ruotsalainen¹,

Renaudin²,

Pei³

et al. 2020

IEEE Intell. Transport. Syst. Mag.

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This review article provides an overview of the use of inertial and visual sensors and discusses their prospects in arctic navigation of autonomous vehicles. We also review the fusion algorithms used so far for integrating vehicle localization measurements as well as the map matching algorithms relating position coordinates with the road infrastructure. The review reveals that the conventional fusion and map matching methods are not enough for navigation in challenging environments, like urban areas and Arctic environment. We also provide new results from testing inertial and optical sensors in vehicle positioning in snowy conditions. We find that the fusion of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) does not provide the accuracy required for automated driving, and the use of optical sensors is challenged by snow covering the road markings. While extensive further research is needed to solve these problems, fusion of GNSS, INS and optical sensors seems to be the best option due to their complementary nature.

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“…To achieve an optimised fusion and a better fault tolerance for the integrated navigation systems, a non‐reset federal Kalman filter (FKF) is utilised for the data fusion. In this paper, a multi‐source navigation system consisting of INS, GNSS and UWB, is used as an example [17–19]. The structure of FKF, shown in Fig.…”

Section: Descriptionsmentioning

confidence: 99%

Fault detection for multi‐source integrated navigation system using fully convolutional neural network

Lian

2018

IET Radar, Sonar & Navigation

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A Variable Proportion Adaptive Federal Kalman Filter for INS/ESGM/GPS/DVL Integrated Navigation System

Cited by 15 publications

References 2 publications

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

Toward Autonomous Driving in Arctic Areas

Fault detection for multi‐source integrated navigation system using fully convolutional neural network

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