Multimodal sensory fusion for soccer robot self-localization based on long short-term memory recurrent neural network

Abstract

Self-localization is a fundamental requirement for autonomous mobile robots. With the rapid development in sensor technology, the sensor suites of robot provide multimodal information that naturally ensures perception robustness, multimodal sensory fusion are able to provide a better solution for enhance the capability of self-localization. This paper proposes a multimodal sensory fusion method based on Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) for RoboCup 3D Simulation league. This approach fuses Inertia Navigation System (INS) and vision perceptor information from different sensors at feature level instead of raw data. The experiment results demonstrate that the proposed approach makes an improvement in predictive accuracy and efficiency compared with the standard Extended Kalman Filter (EKF) and the static Particle Filter (PF) methods.