Multi Sensor Fusion Based on Adaptive Kalman Filtering

Yazdkhasti, Setareh; Sąsiadek, Jurek Z.

doi:10.1007/978-3-319-65283-2_17

Cited by 31 publications

(25 citation statements)

References 12 publications

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“…In [28], a combination of a fuzzy logic controller and a conventional Kalman filter for an INS/GPS is proposed for the correction of both the process noise covariance and the measurement noise covariance. The algorithm was validated using a simulation with an EKF, UKF and an Iterated EKF.…”

Section: Related Workmentioning

confidence: 99%

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Osman

Hussein

Al-Kaff

et al. 2019

Sensors

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Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.

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Section: Related Workmentioning

confidence: 99%

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Osman

Hussein

Al-Kaff

et al. 2019

Sensors

View full text Add to dashboard Cite

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“…The detection rate of the faults injected was 100%, however, the diagnosis and recovery rate is lower at 60%. The study in [32] proposed two new extensions to the Kalman filter, the Fuzzy Adaptive Iterated Extended Kalman Filter and the Fuzzy Adaptive Unscented Kalman Filter in order to make the fusion process more resistant to noise. In [17], the authors used the extended Kalman filter in combination with Bayesian method and managed to detect and predict not only individual failures but also simultaneous occurring failures, however, no fault handling was proposed.…”

Section: Sensor Fusion and Conflict Handlingmentioning

confidence: 99%

Proactive Middleware for Fault Detection and Advanced Conflict Handling in Sensor Fusion

Neyens

Zampunieris

2019

Artificial Intelligence and Soft Computing

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Robots traditionally have a wide array of sensors that allow them to react to the environment and make appropriate decisions. These sensors can give incorrect or imprecise data due to malfunctioning or noise. Sensor fusion methods try to overcome some of these issues by using the data coming from different sensors and combining it. However, they often don't take sensor malfunctioning and a priori knowledge about the sensors and the environment into account, which can produce conflicting information for the robot to work with. In this paper, we present an architecture and process in order to overcome some of these limitations based on a proactive rule-based system.

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“…The detection rate of the faults injected was 100%, however the diagnosis and recovery rate is lower at 60%. The study in (Yazdkhasti and Sasiadek, 2018) proposed two new extensions to the kalman filter, the Fuzzy Adaptive Iterated Extended Kalman Filter and the Fuzzy Adaptive Unscented Kalman Filter in order to make the fusion process more resistant to noise. In (Kordestani et al, 2018), the authors used the extended kalman filter in combination with bayesian method and man-aged to detect and predict not only individual failures but also simultaneous occurring failures, however no fault handling was proposed.…”

Section: State Of the Artmentioning

confidence: 99%

Proactive Model for Handling Conflicts in Sensor Data Fusion Applied to Robotic Systems

Neyens

Zampunieris

2019

Proceedings of the 14th International Conference on Software Technologies

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Robots have to be able to function in a multitude of different situations and environments. To help them achieve this, they are usually equipped with a large set of sensors whose data will be used in order to make decisions. However, the sensors can malfunction, be influenced by noise or simply be imprecise. Existing sensor fusion techniques can be used in order to overcome some of these problems, but we believe that data can be improved further by computing context information and using a proactive rule-based system to detect potentially conflicting data coming from different sensors. In this paper we will present the architecture and scenarios for a generic model taking context into account.

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Multi Sensor Fusion Based on Adaptive Kalman Filtering

Cited by 31 publications

References 12 publications

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Proactive Middleware for Fault Detection and Advanced Conflict Handling in Sensor Fusion

Proactive Model for Handling Conflicts in Sensor Data Fusion Applied to Robotic Systems

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