Name
Abstract | ||
|---|---|---|
This paper addresses the improved method for sonar sensor modeling which reduces the specular reflection uncertainty in the occupancy grid. Such uncertainty reduction is often required in the occupancy grid mapping where the false sensory information can lead to poor performance. Here, a novel algorithm is proposed which is capable of discarding the unreliable sonar sensor information generated due to specular reflection. Further, the inconsistency estimation in sonar measurement has been evaluated and eliminated by fuzzy rules based model. To achieve the grid map with improved accuracy, the sonar information is further updated by using a Bayesian approach. In this paper the approach is experimented for the office environment and the model is used for grid mapping. The experimental results show 6.6% improvement in the global grid map and it is also found that the proposed approach is consuming nearly 16.5% less computation time as compared to the conventional approach of occupancy grid mapping for the indoor environments. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.robot.2012.07.003 | Robotics and Autonomous Systems |
Keywords | Field | DocType |
Sonar sensor modeling,Specular reflection,Grid mapping,Sensor fusion,Bayesian theorem | Computer vision,Grid reference,Simulation,Computer science,Specular reflection,Fuzzy logic,Sonar,Sensor fusion,Artificial intelligence,Grid,Uncertainty reduction theory,Occupancy grid mapping | Journal |
Volume | Issue | ISSN |
60 | 10 | 0921-8890 |
Citations | PageRank | References |
3 | 0.39 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. S. Nagla | 1 | 7 | 1.31 |
| moin uddin | 2 | 21 | 5.91 |
| Dilbag Singh | 3 | 67 | 15.16 |