Name
Abstract | ||
|---|---|---|
This paper investigates the moving robot localisation problem using a Doppler–Azimuth radar array. The solution is formulated in the framework of nonlinear/non-Gaussian estimation using a particle filter and a random finite set (RFS) model of measurements. The proposed approach assumes the availability of a feature-based map, radar measurements and robot odometry data. The associations between the measurements and the features of the map (landmarks) are unknown. The RFS model is adopted to deal with false and missed detections and uses Murty’s algorithm to reduce computation when solving the association problem. The proposed particle filter incorporates the Kullback–Leibler Distance (KLD)-Sampling to reduce computational time. Monte-Carlo simulation results demonstrate the efficacy of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.automatica.2018.08.012 | Automatica |
Keywords | Field | DocType |
Monte Carlo localisation,Particle filter,Random finite sets,Mobile robot navigation,Doppler radar | Radar,Doppler radar,Monte Carlo method,Control theory,Particle filter,Algorithm,Odometry,Azimuth,Mobile robot navigation,Mobile robot,Mathematics | Journal |
Volume | Issue | ISSN |
97 | 1 | 0005-1098 |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robin P. Guan | 1 | 4 | 1.52 |
| Branko Ristic | 2 | 711 | 62.37 |
| Liuping Wang | 3 | 10 | 2.48 |
| Rob Evans | 4 | 4 | 1.52 |