3D定位中UWB與IMU兩種融合方法的對比-Paper_ICE_Miraglia_Maleki_Hook

Absfract- There currently exists a high demand for vehicleswith ever increasing levels of autonomy. While some of thesevehicles may depend on GPS for localization, other vehicles willrequire a more precise localization solution in order to performtheir tasks. In addition, some vehicles may need to work in GPSdenied environments. These issues could be addressed with the useof an Ultra-Wide Band (UWB) ranging sensor fused with anInertial-Measurement-Unit (IMU) using an Extended KalmanFilter (EKF). The main goal of this work is to investigate andcompare two different sensor data fusion techniques toincorporate a 3-axis 9-DOF IMU in a tightly coupled fashion to a3-D positioning solution that is derived from UWB signals. Thetechniques differ from each other in that the first fusion of theIMU data occurs in the prediction step and the second fusionoccurs in the update step. Experimental results obtained with aquadcopter show that the data fusion performed in the update stepoutperforms the fusion performed in the prediction step. Theresults also show that when using very accurate UWB rangingsensors, the use of IMU data does not improve significantly theaccuracy of the position. However, the integration of IMU data inthe update step increases the robustness of the EKF againsterroneous modelling of the process noise.

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