Multisensor Data Fusion and Integration for Mobile Robots: A Review

A New Approach to Self-Localization for Mobile Robots Using Sensor Data Fusion

This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such ...

Multisensor Fusion and Integration

Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview...

A New Method for Multisensor Data Fusion Based on Wavelet Transform in a Chemical Plant

This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter...

A Tutorial on Multisensor Integration and Fusion

ion. Symbol-level fusion may be the only means by which sensory information can be fused if the information provided by the sensors is very dissimilar or refers to different regions in the environment. The symbols used for fusion can originate either from the processing of the information provided by the sensors in the system, or through symbolic reasoning processes that may make we of a priori...

Multisensor Data Fusion