Abstract
A novel WSN-aided SLAM (simultaneous localization and mapping) method is proposed for mobile robot to solve two main problems in traditional SLAM methods, i.e., high dimensions in problem spaces and difficulties in multi-target data association. Model for the WSN-aided SLAM method is built, and noises are analyzed. Then a distributed particle filtering (PF) data fusion algorithm suitable for this method is developed. The key steps, such as particle initialization, prediction, sequential importance sampling, resampling, are particularly analyzed, and the validity and efficiency of the method are testified by simulation experiment. The experiment results demonstrate that, when the PF algorithm is used and the WSN is integrated for aided navigation, the dimensions of problem spaces can be greatly reduced, the multi-target data association problems can be solved and blind nodes can be located with high precision independent of the anchor node. The precision of localization and mapping for mobile robots can be effectively improved, and the error accumulation of inertial navigation can be effectively suppressed especially when the robot closed-loop track is not required.
| Original language | English |
|---|---|
| Journal | Jiqiren/Robot |
| Volume | 30 |
| Issue number | 5 |
| Publication status | Published - 1 Sept 2008 |
| Externally published | Yes |
Keywords
- Mobile robot
- Particle filtering (PF)
- Simultaneous localization and mapping (SLAM)
- Wireless sensor network (WSN)
ASJC Scopus subject areas
- Software
- Modelling and Simulation
- Mechanical Engineering
- Artificial Intelligence
- Applied Mathematics
- Electrical and Electronic Engineering