Name
Abstract | ||
|---|---|---|
Computationally efficient SLAM (CESLAM) has been proposed to solve simultaneous localization and mapping problem in real-time design. CESLAM first uses the landmark measurement with the maximum likelihood to update the particle states and then update their associated landmarks later. This improves the accuracy of localization and mapping by avoiding unnecessary comparisons. This paper describes a modified version of CESLAM called rapidly operations SLAM (ROSLAM) which improves the runtime even further. We present an empirical evaluation of ROSLAM in a simulated environment which shows that it speeds up previous well known algorithms by 100 %. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-31293-4_6 | ROBOT INTELLIGENCE TECHNOLOGY AND APPLICATIONS 4 |
Keywords | DocType | Volume |
FastSLAM,CESLAM,Particle filter,Extended Kalman filter | Conference | 447 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teng-Wei Huang | 1 | 1 | 0.70 |
| Hsu, Chen-Chien | 2 | 0 | 1.35 |
| Wei-Yen Wang | 3 | 995 | 87.40 |
| Jacky Baltes | 4 | 294 | 57.76 |