Name
Abstract | ||
|---|---|---|
Robots play more and more important roles in our daily life. To better complete assigned tasks, it is necessary for the robots to have the ability to recognize their self-activities in real time. To perceive the environment, robots usually equipped with rich sensors, which can be used to recognize their self-activities. However, the intrinsics of the sensors such as accelerometer, servomotor and gyroscope may have significant differences, individual sensor usually exhibits weak performance in perceiving the environment. Therefore, multi-sensor fusion becomes a promising technique so that to achieve better performance. In this paper, facing the issue of robot self-activity recognition, we propose a framework to fuse information from multiple sensory streams. Our framework takes Recurrent Neural Network(RNN) that uses Long Short-Term Memory(LSTM) units to model temporal information conveyed in multiple sensory streams. In the architecture, a hierarchy structure is used to learn the sensor-specific features, a shared layer is used to fuse the features extracted from multiple sensory streams. We collect a dataset on PKU-HR6.0 robot to evaluate the proposed framework. The experiment results demonstrate the effectiveness of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/HUMANOIDS.2018.8624918 | 2018 IEEE-RAS 18TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS) |
Field | DocType | ISSN |
Computer vision,Activity recognition,Accelerometer,Simulation,Computer science,Recurrent neural network,Sensor fusion,Artificial intelligence,Robot,Fuse (electrical),Intrinsics,Servomotor | Conference | 2164-0572 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dingsheng Luo | 1 | 46 | 11.61 |
| Yang Ma | 2 | 0 | 0.34 |
| Xiangqi Zhang | 3 | 0 | 0.34 |
| Xihong Wu | 4 | 279 | 53.02 |