Research Article
Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model
Table 2
Related works: an RFID-based localization system.
| Works | Sensors | Dense of tags | Manually built tag map | Robot localization | Tag localization | SLAM | Precision (mm) |
| Yang et al. [24] | UHF-RFID | 9 tags/m2 | ✓ | Hybrid particle filter | ✗ | ✗ | 186 | Hähnel et al. [25] | UHF-RFID+LRF | Attached to objects | ✗ | Monte Carlo localization (with known tag position) | Particle filter (with known robot route) | ✗ | Much better than only using a laser | Joho et al. [26] | UHF-RFID | 350 tags | ✗ | Monte Carlo localization (with known tag position) | Particle filter (with known robot route) | ✗ | 355 | Kleiner et al. [15] | UHF-RFID+odometry | 10tags/90,000 m2 | ✗ | Graph optimization | Graph optimization | ✓ | 5,640 | Kodaka et al. [27] | LF-RFID | Lattice, 16 tags/m2 | ✓ | Particle filter | ✗ | ✗ | <100 | Yang and Wu [28] | HF-RFID | 100 tags/m2 | ✓ | Particle filter | ✗ | ✗ | 53 | Mi and Takahashi [29] | HF-RFID | 100 and 16 tags/m2 | ✗ | Monte Carlo localization | ✗ | ✗ | 25 and 22 | Wang and Takahashi [16] | HF-RFID | 100 and 16 tags/m2 | ✗ | Particle filter | Particle filter | ✓ | 14.5 and 21 (with detection range of 30) | Our approach | HF-RFID | 4 tags/m2 | ✗ | Particle filter | Particle smoother | ✓ | >25 and <55 (with detection range from 50 to 250) |
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