Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s12652-017-0483-7 | J. Ambient Intelligence and Humanized Computing |
Keywords | Field | DocType |
Robot self-localization, Multimodal sensory fusion, Long short-term memory, Recurrent neural network | Computer vision,Extended Kalman filter,Computer science,Simulation,Particle filter,Navigation system,Recurrent neural network,Robustness (computer science),Artificial intelligence,Soccer robot,Robot,Mobile robot | Journal |
Volume | Issue | ISSN |
8 | 6 | 1868-5145 |
Citations | PageRank | References |
1 | 0.36 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhuan Lu | 1 | 17 | 12.30 |
| Ju Zhang | 2 | 6 | 7.56 |
| Xinli Zhao | 3 | 1 | 0.36 |
| Jianrong Wang | 4 | 17 | 5.69 |
| Jianwu Dang | 5 | 293 | 91.90 |