Research on Multisensor Fusion State Estimation of Automatic Navigation Vehicle Based on RNN Model

Multi sensor fusion state estimation of autonomous navigation vehicle based on RNN model. Multi sensor fusion is a process of combining information from different sensors to estimate a single value. In this paper, we propose a recursive neural network (RNN) model to estimate the state of multiple sensors. This method uses RNN architecture with two hidden layers and one output layer to combine sensor data. The input data is first divided into a sequence and then input into the RNN model to predict the output. We use three types of sensors: IMU, LIDAR and GPS. First, the RNN model is trained using three sensors for the navigation system. Then, we use this trained model to estimate the current position using only one sensor data. Finally, we combine the data of all three sensors to obtain the final estimated position of the vehicle at any given time step.