Name
Abstract | ||
|---|---|---|
Computationally Efficient SLAM (CESLAM) was proposed to improve the accuracy and runtime efficiency of FastSLAM 1.0 and FastSLAM 2.0. This method adopts the landmark measurement with the maximum likelihood, where the particle state is updated before updating the landmark estimate. Also, CESLAM solves the problem of real-time performance. In this paper, a modified version of CESLAM, called adaptive computation SLAM (ACSLAM), as an adaptive SLAM enhances the localization and mapping accuracy along with better runtime performance. In an empirical evaluation in a rich environment, we show that ACSLAM runs about twice as fast as FastSLAM 2.0 and increases the accuracy of the location estimate by a factor of two. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-31293-4_7 | ROBOT INTELLIGENCE TECHNOLOGY AND APPLICATIONS 4 |
Keywords | Field | DocType |
Fastslam,CESLAM,Particle filter,Extended kalman filter | Computer vision,Extended Kalman filter,Computer science,Particle filter,Algorithm,Maximum likelihood,Moving horizon estimation,Artificial intelligence,Landmark,Simultaneous localization and mapping,Computation | Conference |
Volume | ISSN | Citations |
447 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Da-Wei Kung | 1 | 0 | 0.34 |
| Hsu, Chen-Chien | 2 | 0 | 1.35 |
| Wei-Yen Wang | 3 | 995 | 87.40 |
| Jacky Baltes | 4 | 294 | 57.76 |