An autonomous guided vehicle for cargo handling applications
International Journal of Robotics Research
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Distributed consensus algorithms for merging feature-based maps with limited communication
Robotics and Autonomous Systems
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This paper presents an efficient method of multi-sensorestimation that can be used with asynchronous and synchronoussensors. A decentralized architecture is used for the fusion ofinformation obtained from several asynchronous measurements. Theissue of the synchronization of the information, which is critical inthe proposed method, is addressed. The information form of the Kalmanfilter (information filter) is used as the main algorithm forestimation. The method is demonstrated with the implementation of anavigation system for an autonomous land vehicle. The integrity issueis also addressed with the implementation of multiple independentestimation loops. The proposed method allows for efficient fusion ofinformation obtained from different measurements for covariancereduction, while providing the benefits of decentralized estimationarchitecture for integrity purposes. The resulting estimates areequivalent to an optimal centralized filter when the loopsincorporate all the information available in the system. Theinformation obtained from each measurement is then broadcast to theother loops after being synchronized. This information is used in anassimilation stage to achieve more accurate estimates. Theassimilation frequency is also discussed considering the trade off offault detectability and estimation covariance reduction. Theperformance of the navigation method is examined by comparing the resulting position estimates to those of independent navigationloops.